{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/israeldi/quantlab/master/assets/images/Program-Logo.png\" width=\"400px\" align=\"right\">\n",
    "\n",
    "# Financial Data Structures\n",
    "### [(Go to Quant Lab)](https://israeldi.github.io/quantlab/)\n",
    "\n",
    "#### Source: Advances in Financial Machine Learning\n",
    "\n",
    "&copy; MARCOS LOPEZ DE PRADO\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/israeldi/quantlab/master/assets/images/advances_fin_machine_learning.jpg\" width=\"200px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code and Other work by: [ad1m](https://github.com/ad1m/Financial_Machine_Learning/blob/master/Financial_Data_Structures.ipynb) and [fernandodelacalle](https://github.com/fernandodelacalle/adv-financial-ml-marcos-exercises/tree/master/notebooks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Introduction:</h4>\n",
    "<p>In this notebook we learn to work with unstructured financial data and from this to derive a structured dataset for machine learning algorithms. Generally, it is not advisable to consume someone else's preproccessed dataset as the likely outcome will be that you are figuring out what they have already figured out. We want to take an unstructured dataset and process it such that we can find novel informative features.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Structured V.S. Unstructured Data:</h4>\n",
    "<p>Structured data is data that you would usually find in a relational database. For instance: phone numbers, Social Security numbers, or ZIP codes. Even text strings of variable length like names are contained in records, making it a simple matter to search. Unstructured Data is data that is in the wild that does not have a concrete structure. For instance, social media text feeds, audio, and images.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>The 4 Types of Financial Data:</h4>\n",
    "<p>There are four types of financial data:</p>\n",
    "<ul>\n",
    "    <li><b>Fundamental Data:</b> Data you can find in regulatory findings such as quarterly reports. For example: Assets, liabilities, sales, costs, and earnings. This data is extremly regularized and low frequency. Since this data is so accessible to the marketplace, it is unlikely that there is much value left to be exploited. However, it can be useful in combination with other data types.</li>\n",
    "    <li><b>Market Data:</b> Data from all trading activity that takes place in an exchange such as price, volatility, dividends, and volume.</li>\n",
    "    <li><b>Analytics:</b> Data that has already been proccessed in a particular way such as analyst recommendations, credit ratings, earnings expectations, and news sentiment. This is data that is usually purchased from an alternate vendor.</li>\n",
    "    <li><b>Alternative Data:</b> Primary information that has not made it yet to other sources such as sattelite images, google searches, and twitter posts. This data is usually more unique and harder to process.</li>\n",
    "</ul>\n",
    "<p><b>Note:</b> A dataset might be useful if it annoys the data infrastructure team. Perhaps your competitors did not try to use it for particular reasons or gave up midway.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Bars:</h4>\n",
    "<p>The data structures used to contain trading information are often referred to as bars. This is basically a table of data and the rows contain information. These rows are the \"bars\". These bars can vary greatly in how they were constructed but in general there are two categories of bars:</p>\n",
    "<ul>\n",
    "    <li>Standard Bar Methods</li>\n",
    "    <li>Information-Driven Methods</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Standard Bars:</h4>\n",
    "<p>Standard Bars aim to transform a series of observations that arrive at an irregular frequency into a homogenous series derived from regular sampling. There are 4 main type of standard bars:</p>\n",
    "<ul>\n",
    "    <li>Time Bars</li>\n",
    "    <li>Tick Bars</li>\n",
    "    <li>Volume Bars</li>\n",
    "    <li>Dollar Bars</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Time Bars:</h5>\n",
    "<p>Time Bars are obtained by sampling information at a fixed time interval e.g. once every minute. This information usually contains:</p>\n",
    "<ul>\n",
    "    <li>Timestamp</li>\n",
    "    <li>Volume</li>\n",
    "    <li>VWAP - calculated by adding up the dollars traded for every transaction (price multiplied by number of shares traded) and then dividing by the total shares traded for the day.</li>\n",
    "    <li>Open</li>\n",
    "    <li>Close</li>\n",
    "    <li>High</li>\n",
    "    <li>Low</li>\n",
    "</ul>\n",
    "<p>This is the typical csv data that you will find from yahoo finance for a particular equity. This type of data should be avoided for two reasons:</p>\n",
    "<ol>\n",
    "    <li>Markets do not process information at a constant time interval e.g. the hour of the open is more active than the hour around noon. Here time bars oversample information during low-activity periods and undersample information during high-activity periods.</li>\n",
    "    <li>Time sampled series often exhibit poor statistical properties.</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Tick Bars:</h5>\n",
    "<p>Sample variables such as Timestamp, VWAP, open price, etc. are extracted each time a pre-defined number of transactions takes place.</p>\n",
    "\n",
    "<p>For instance, every 1000 transactions we take a sample bar. Mandlebrot and Taylor realized that sampling as a function of the number of transactions gives more desirable statistical properties; sampling as a function of trading activity allows us to achieve returns closer to Independant and Identitically Distributed (IID) Normal. Many statistical methods make an assumption that observations are drawn from an IID Gaussian process so this allows us to take advantage of these statistical observations.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Volume Bars:</h5>\n",
    "<p>Volume bars sample every time a pre-defined amount of the securitie's units (shares, futures contracts, etc.) have been exchanged. For example, we could sample prices every time a futures contract exchanges 1,000 units, regardless of the number of ticks involved. Volume bars circumvent the following problem that tick bars incur:</p>\n",
    "\n",
    "<p>Suppose there is one order sitting on the offer for a size of 10. If we buy 10 lots, the order will be recorded as 1 tick. If there are 10 orders of size 1, our 1 buy will be recorded as 10 separate transactions.</p>\n",
    "\n",
    "<p>Volume bars are preferred over tick bars as sampling by volume gets us closer to an IID Gaussian distribution than sampling by tick bars.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Dollar Bars:</h5>\n",
    "<p>Dollar bars are formed by sampling an observation every time a pre-defined market value is exchanged.</p>\n",
    "<p>The number of shares traded is a function of the actual value exchanged. Thus, it makes sense to sample bars in terms of dollar value exchanged rather than ticks or volume particularly when the analysis involves significant price fluctuations.</p>\n",
    "\n",
    "<p>Dollar bars are also more interesting than time, tick, or volume bars since the number of outstanding shares often changes multiple times over the course of a securitie's life as a result of corporate actions. Even after adjustment for splits and reverse splits, there are other actions that will impact the amount of ticks and volumes, like issuing new shares or buying back existing shares. Dollar bars tend to be robust in the face of those actions.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "\n",
    "- Download the data from: http://www.kibot.com/buy.aspx at the: \"Free historical data for your data quality analysis\" section\n",
    "- We have the data from the WDC stock and the iShares IVE ETF: https://www.ishares.com/us/products/239728/ishares-sp-500-value-etf "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tick Data info from kibot\n",
    "- http://www.kibot.com/support.aspx#data_format\n",
    "- The order of the fields in the tick files (with bid/ask prices) is: Date, Time, Price, Bid, Ask, Size. \n",
    "- The bid/ask prices are recorded whenever a trade occurs and they represent the \"national best bid and offer\" (NBBO) prices across multiple exchanges and ECNs.\n",
    "- For each trade, current best bid/ask values are recorded together with the transaction price and volume. Trade records are not aggregated and all transactions are included in their consecutive order.\n",
    "- The order of fields in our regular tick files (without bid/ask) is: Date,Time,Price,Size.\n",
    "- The order of fields in our 1, 5 or 10 second files is: Date,Time,Open,High,Low,Close,Volume. It is the same format used in our minute files.\n",
    "- The stocks and ETFs data includes pre-market (8:00-9:30 a.m. ET), regular (9:30 a.m.-4:00 p.m. ET.) and after market (4:00-6:30 p.m. ET) sessions.\n",
    "- Trading for SPY (SPDR S&P 500 ETF) and some other liquid ETFs and stocks usually starts at 4 a.m and ends at 8 p.m. ET."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially import all the modules we will be using for our notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Modules for Dataframes\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "\n",
    "# Module for plotting\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = 10,8\n",
    "\n",
    "# Here we install these packages in order to use them later in the notebook\n",
    "import sys\n",
    "# !{sys.executable} -m pip install statsmodels\n",
    "# !{sys.executable} -m pip install pyarrow\n",
    "# !{sys.executable} -m pip install mpl_finance\n",
    "# !{sys.executable} -m pip install seaborn\n",
    "\n",
    "# These Modules are for compressing the large data set\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "\n",
    "import os\n",
    "\n",
    "# Import our user-written Functions \n",
    "import ml_functions as ml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create Directories to save results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directory where we will save our plots\n",
    "directory = \"./data\"\n",
    "if not os.path.exists(directory):\n",
    "    os.makedirs(directory)\n",
    "\n",
    "directory = \"./images\"\n",
    "if not os.path.exists(directory):\n",
    "    os.makedirs(directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can apply `prepare_data_kibot` function that reformats the column names and the time index. Using `to_parquet` method in order to compress the data. Here we download and save data to the `data` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m/Volumes/GoogleDrive/My Drive/Documents Google Drive/Masters/Quant Files/Friday Workshops/Fall 2019/Advances in Machine Learning Solutions/ml_functions.py\u001b[0m in \u001b[0;36mprepare_data_kibot\u001b[0;34m(raw_data_path, out_data_path)\u001b[0m\n\u001b[1;32m     43\u001b[0m     df = pd.read_csv(raw_data_path,\n\u001b[1;32m     44\u001b[0m                      \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m                      names=['day', 'time', 'price', 'bid', 'ask', 'vol'])\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     df['date'] = pd.to_datetime(df['day'] + df['time'],\n",
      "\u001b[0;32m/Users/israeldiego/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/israeldiego/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    422\u001b[0m     \u001b[0mcompression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m     filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(\n\u001b[0;32m--> 424\u001b[0;31m         filepath_or_buffer, encoding, compression)\n\u001b[0m\u001b[1;32m    425\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'compression'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/israeldiego/anaconda3/lib/python3.6/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[0;34m(filepath_or_buffer, encoding, compression, mode)\u001b[0m\n\u001b[1;32m    198\u001b[0m             \u001b[0;31m# Override compression based on Content-Encoding header\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0mcompression\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'gzip'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m         \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/israeldiego/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    460\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m                     \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_safe_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIncompleteRead\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_close_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/israeldiego/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_safe_read\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    610\u001b[0m         \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    611\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mamt\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 612\u001b[0;31m             \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMAXAMOUNT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    613\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mIncompleteRead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/israeldiego/anaconda3/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "raw_data_path = 'http://api.kibot.com/?action=history&symbol=IVE&interval=tickbidask&bp=1&user=guest'\n",
    "out_data_path = './data/IVE_tickbidask.parq'\n",
    "ml.prepare_data_kibot(raw_data_path, out_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#out_data_path = '../data/interim/WDC_tickbidask.parq'\n",
    "out_data_path = './data/IVE_tickbidask.parq'\n",
    "df = pd.read_parquet(out_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "_ = ax.boxplot(df.price)\n",
    "plt.savefig('./images/boxplot_01.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mad = ml.mad_outlier(df.price.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "print(mad.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "### drop the incorrect rows\n",
    "df = df.loc[~mad]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "_ = ax.boxplot(df.price)\n",
    "plt.savefig('./images/boxplot_02.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean_data_path = '../data/processed/clean_WDC_tickbidask.parq'\n",
    "clean_data_path = './data/clean_IVE_tickbidask.parq'\n",
    "df.to_parquet(clean_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test clean data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean_data_path = '../data/processed/WDC_tickbidask.parq'\n",
    "clean_data_path = './data/clean_IVE_tickbidask.parq'\n",
    "df = pd.read_parquet(clean_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>vol</th>\n",
       "      <th>dollar_vol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:30:00</th>\n",
       "      <td>50.79</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>100</td>\n",
       "      <td>5079.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:30:00</th>\n",
       "      <td>50.71</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>638</td>\n",
       "      <td>32352.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:32</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.75</td>\n",
       "      <td>50.76</td>\n",
       "      <td>100</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:33</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.72</td>\n",
       "      <td>50.75</td>\n",
       "      <td>100</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:50</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.73</td>\n",
       "      <td>50.76</td>\n",
       "      <td>300</td>\n",
       "      <td>15225.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price    bid    ask  vol  dollar_vol\n",
       "date                                                     \n",
       "2009-09-28 09:30:00  50.79  50.70  50.79  100     5079.00\n",
       "2009-09-28 09:30:00  50.71  50.70  50.79  638    32352.98\n",
       "2009-09-28 09:31:32  50.75  50.75  50.76  100     5075.00\n",
       "2009-09-28 09:31:33  50.75  50.72  50.75  100     5075.00\n",
       "2009-09-28 09:31:50  50.75  50.73  50.76  300    15225.00"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "#### ([Back to Top](#Table-of-Contents))\n",
    "\n",
    "**2.1** On a series of E-mini S&P 500 futures tick data:\n",
    "- **(a)** Form tick, volume, and dollar bars. Use the ETF trick to deal with the roll.\n",
    "- **(b)** Count the number of bars produced by tick, volume, and dollar bars on a\n",
    "weekly basis. Plot a time series of that bar count. What bar type produces\n",
    "the most stable weekly count? Why?\n",
    "- **(c)** Compute the serial correlation of returns for the three bar types. What bar\n",
    "method has the lowest serial correlation?\n",
    "- **(d)** Partition the bar series into monthly subsets. Compute the variance of returns\n",
    "for every subset of every bar type. Compute the variance of those variances.\n",
    "What method exhibits the smallest variance of variances?\n",
    "- **(e)** Apply the Jarque-Bera normality test on returns from the three bar types. What method achieves the lowest test statistic?\n",
    "\n",
    "**2.2** On a series of E-mini S&P 500 futures tick data, compute dollar bars\n",
    "and dollar imbalance bars. What bar type exhibits greater serial correlation?\n",
    "Why?\n",
    "\n",
    "**2.3** On dollar bar series of E-mini S&P 500 futures and Eurostoxx 50 futures:\n",
    "- **(a)** Apply Section 2.4.2 to compute the {$\\hat{\\omega}_t$} vector used by the ETF trick. (Hint: You will need FX values for EUR/USD at the roll dates.)\n",
    "- **(b)** Derive the time series of the S&P 500/Eurostoxx 50 spread.\n",
    "- **(c)** Confirm that the series is stationary, with an ADF test.\n",
    "\n",
    "**2.4** Form E-mini S&P 500 futures dollar bars:\n",
    "- **(a)** Compute Bollinger bands of width 5% around a rolling moving average. Count how many times prices cross the bands out (from within the bands to outside the bands).\n",
    "- **(b)** Now sample those bars using a CUSUM filter, where {$y_t$} are returns and h = 0.05. How many samples do you get?\n",
    "- **(c)** Compute the rolling standard deviation of the two-sampled series. Which one is least heteroscedastic? What is the reason for these results?\n",
    "\n",
    "**2.5** Using the bars from exercise 4:\n",
    "- **(a)** Sample bars using the CUSUM filter, where {$y_t$} are absolute returns and h = 0.05.\n",
    "- **(b)** Compute the rolling standard deviation of the sampled bars.\n",
    "- **(c)** Compare this result with the results from exercise 4. What procedure delivered the least heteroscedastic sample? Why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean_data_path = '../data/processed/clean_WDC_tickbidask.parq'\n",
    "clean_data_path = './data/clean_IVE_tickbidask.parq'\n",
    "df = pd.read_parquet(clean_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>vol</th>\n",
       "      <th>dollar_vol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:30:00</th>\n",
       "      <td>50.79</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>100</td>\n",
       "      <td>5079.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:30:00</th>\n",
       "      <td>50.71</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>638</td>\n",
       "      <td>32352.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:32</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.75</td>\n",
       "      <td>50.76</td>\n",
       "      <td>100</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:33</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.72</td>\n",
       "      <td>50.75</td>\n",
       "      <td>100</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:50</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.73</td>\n",
       "      <td>50.76</td>\n",
       "      <td>300</td>\n",
       "      <td>15225.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price    bid    ask  vol  dollar_vol\n",
       "date                                                     \n",
       "2009-09-28 09:30:00  50.79  50.70  50.79  100     5079.00\n",
       "2009-09-28 09:30:00  50.71  50.70  50.79  638    32352.98\n",
       "2009-09-28 09:31:32  50.75  50.75  50.76  100     5075.00\n",
       "2009-09-28 09:31:33  50.75  50.72  50.75  100     5075.00\n",
       "2009-09-28 09:31:50  50.75  50.73  50.76  300    15225.00"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the montly candlestick plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ohlc = df.price.resample('1M').ohlc() \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ml.plot_candlestick_ohlc(df_ohlc, ax, width=18)\n",
    "plt.title(\"S&P 500 Montly candlestick\")\n",
    "plt.savefig('./images/monthly_candlestick.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.1** On a series of E-mini S&P 500 futures tick data:\n",
    "\n",
    "**(a)** Form tick, volume, and dollar bars. Use the ETF trick to deal with the roll."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "minute_df = df.price.resample('min').ohlc().dropna()\n",
    "\n",
    "m = 10\n",
    "tick_df = ml.tick_bar(df, m)\n",
    "\n",
    "m = 1_000\n",
    "vol_df = ml.volume_bar(df, m)\n",
    "\n",
    "m = 100_000\n",
    "dollar_df = ml.dollar_bar(df, m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1651077, 5) (598513, 4) (165108, 5) (470932, 6) (419254, 6) 2009-09-28 09:30:00, 2019-09-11 15:59:59\n"
     ]
    }
   ],
   "source": [
    "print(f\"{df.shape} {minute_df.shape} {tick_df.shape} {vol_df.shape} {dollar_df.shape} {tick_df.index[0]}, {tick_df.index[-1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X8atf/Qq/3x8ez87OZuXKldxwww0MGzaMXbt2dcZHISIiIiIiItIuVVVLqapaGnG8vO4v9OtTF3F8bCrcMeLSaITWK6kkpROYez+P6vWXLFlCYWFhiyV4J7v//vs5evQor7zyCjk5ORw4cKDFnIaGBu655x5iY2NZs2YNCQkJEa+3Zs0a1q5dS0JCAgsXLmTBggWsWrUKgAEDBvCHP/yBnJwcvvjiC2699VYGDhzID3/4w/D5BQUFrFy5kpycHHw+3+l/ACIiIiIiIiId4HRuweXaGv755CqoWk8t2/bfw4jktq/TWLscUq6LVpi9iiqgTlPgzZfx3zkF/51ToKocqsrDrwNvvtzl8VRWVrJu3Toee+wxBg0ahGEYDBkyhCFDhoTnHDlyhGnTppGbm8vKlSvbTD4BLFq0iH79+mG32/nZz37GBx98wJEjRwC48cYbw/e58MILmTFjBh999FGz8++++25yc3OJiYkhPj6+89+0iIiIiIiISCtOrHxqrQpq2c5l/Ot2NztrW567sxYmvR/8s+rouGiG2asoAXWaLFPmYExuuRbUmDwby5Q5XR5PcXExAEOHDo0455133qG8vJwFCxZgsZz6V5+Tk9Pi58OHDwPw+uuv873vfY8LLriAkSNH8vzzz1NVVRXxfBEREREREZGucGL1E4DLtTXcDyqkYG8BF6cEl9mdLNSAHKCgsCCaofYqSkCdAeNbE1oeu7TlsU65l2G0OR5K9hQVFUWcM3fuXPLz85k+fTqlpaWnvGcoqXXizwMGDKC0tJQFCxbw4x//mB07dvDVV19x2223tTi/PUkuERERERERkc7UWsXTicdcPhfV7mrm5ka+Rmisyl2F2+fu3AB7KWUIzoC5/SOwp2C5+9+w3P1vYE/B/NvmqNyrX79+rfZ0CsnMzOTGG29k8eLFFBcXY5omRUVFLRJSDz/8MDNmzGDq1Kns37+/zXs+9dRTVFRU4HA4+OUvf8mVV17Jeeedh9PpJBAIkJ6eTmxsLNu3b+ePf/xjZ7xNERERERERkdN2cvVTyIlVUAnWBK7sa2+1+ikkVAWVYcvAZrVFK9xeRQmoM2R59BmMSydgXDoBy6PPRO0+d955J7t372bUqFFMmjSp1TlLly5l9OjRzJgxg7y8PG6//fZWd7lbtGgRd911F/n5+ezZsyfiPadPn860adO47LLL8Hq9PP300wAMHz6cBx54gNtvv51Ro0axbNkyvv/973fOGxURERERERE5TW3tenfi2Pzh9lNea24uzB7RsvWOnB7DNE2zu4PoCmVlZa0edzqdJCYmdnE0ciY68juz2+04HI4oRyS9lZ4viSY9XxJtesYkmvR8STTp+ZJInM4tlJTc3OacgQNfBTjlvJDU/v9HP+2CF1FWVla751qjGIeIiIiIiIiISJdoq/qpI3NO5KlfAUpAdQoloERERERERETknGfvt5KZr91AmuUQT449fvyZffDHUjAweHvaI1yUeVF47H/+538oK/Nx4/fvYMyYWDIy7DS5mroh+p5PPaBERERERERE5Jy3bOcyDjkOtdjdLvTaxOS2d47v4O52u/F6vVgsVuwJaSTG2dRwPIqUgBIRERERERGRc17B3gIuTqHF7nbJVpiRHfz5qOto+HhDQwMAiYkpGIaBRRmSqNLHKyIiIiIiIiLntLrGOqrd1S2qn0JOPF7eWA5AfX09pgmJiXYMAyWgokwfr4iIiIiIiIicsxwOB2/88Q3G9YlrUf0UcmIVVL+kfuHzTBOSkvpgsZhdFG3vpQSUiIiIiIiIiJyTysvLeemll6ioqOBfh7Tdv2luLvRP6B9+XV9fTyAQrICKiYlyoKJd8ERERERERETk3PPNN9/wpz/9iaamJkaMcDEktb7N+clWePE73w+/bmhoOLYEr48SUF1AFVAiIiIiIiIick7ZvXs3b7zxBk1NTYwcOZIxY3a067x4T0H451APqKQku5bgdQEloDqJy+fq7hDalJ2dzSeffNLq2JYtWxg0aFAXRyQiIiIiIiLSMaZp8sEHH7B+/XoCgQCXX3453/ve90hOfg23uxy//yjnnVfIc8/9lKSl44lf8R3S0g6Tl1dKXl4pw4Z9Gb6Ww+EgEAj1gOrGN9VLaAneGaj11LJs5zJe2fsKVe4qMmwZzMqbxbyx80iNj9D5TEREREREREROy/bt2/njH/+O1xvLzTdfzYQJF+LzweHDwQzS4M9eJH7pKpaETqjzwuKb8APG5NlYpswBgomsUBNy9YDqGkpAnaZaTy3T102nsKYwfKzKXcXy3ct5r/g91k5e22uTUH6/H8MwsCiFLCIiIiIiIp3owIEyGhsNRo++lrS0MYCPo0dj8PsNkpICJM68hYDNxFxX0Oy8E5NPAI2NjQQCAeLjE7FaY4mJCXTxO+l9lCE4Tct2LmuWfDpRYU0hy3ct77R7/f73v+ef/umfmh07dOgQOTk5FBcXA7Bnzx5uvvlmRo8ezfjx43nyySfx+/0dus/q1asZN24cF1xwAQsXLqSxsTE89qtf/Yrx48czfPhwrrjiCp599tnwWHFxMdnZ2axatYqJEycybNgwKisrz+Adi4iIiIiIiLTU2OglPt4kKSmdhAQTl8ugqsqCYUBWVvA7sPGtCS3OMy5tfszhcADB/k+AekB1ASWgTlPB3oK2xwvbHu+IqVOnsn//fj7//PPwsdWrVzN+/HhycnKor6/nlltu4YorrmDHjh288MILvPLKK6xYsaLd9/D7/axfv54NGzawadMmvvnmGx599NHweF5eHq+99hp79+7l17/+NY899hibNm1qdo3XXnuN1atXU1hYSEZGxhm/bxEREREREZETNTa6SUoyycuLxW43KS0Nrp3LyPCTkBCcY27/iECSnTfPG031IBfOQQ58n2ymttbAPJZnCiWgEhP7AGgJXhdQAuo0uHwuqt3Vbc6pclfh9rk75X6pqalcd911rF69GgiuVX311VeZPXs2AO+99x6xsbEsXLiQ+Ph4hg8fzr333svLL7/cofs89NBD9OnTh759+/LAAw+wZs0aAoFgGeKMGTM477zzMAyDCRMmcPXVV/PRRx81O/8nP/kJ/fr1Iy4ujhj97RUREREREZFO1tjoASAuzkZNjQWn08BqNenf//gSOrPwMyyNDqYc2YNr/CFqxpdheWsV9p9Opvy5YLFIfX09EGxADkpAdQUloE5DgjWBdFt6m3MybBnYrLZOu+esWbN47bXXaGpq4qOPPqK+vp4bbrgBgLKyMgYOHIhhGOH5gwcPpqysrEP3GDhwYPjnnJwcPB4P1dXBRNvvfvc7rrnmGkaPHs2oUaPYsGEDVVVVzc7Pyck53bcnIiIiIiIickqNjcFCj7i4eAKB4Hfg887zN0sgxfz0V3DTbFw5DtyDGnAPasCV4+Cr83/I7kG3AccroGw2LcHrKkpAnabZebPbHh/R9nhHXXXVVcTFxbF+/XpWr17NlClTSDhWX5iVlUVJSQmmefwvzKFDh8jKyurQPUpKSsI/FxcXEx8fT3p6Op9++im//OUveeyxx/jss8/48ssvufbaa5vdD2iWABMRERERERHpTH6/H5fLi2EYNDYmsG+flYYGSE9vmTyyXDqB6iuOF2VUX1FG2YDvkJ4erJQ6vgQvlIDqgjfQy+kjPk3zxs5jRNqIVsdGpI3g3ovv7dT7xcTEkJ+fz3PPPcfbb78dXn4HcM011+D1enn66afxer3s27eP5cuXc8stt3ToHr/61a9wOBxUVlaydOlSZsyYgcViweFwYLFYyMjIwDAMNmzYwF//+tdOfX8iIiIiIiIibWls9GCaweqnmJjg0ru4uNYrlxq/fB73oIbwa/egBkYmrAwv1SssbOSbb2JobAzuXq8leNGnBNRpSo1PZe3ktcy7eB4ZtmDD7QxbBvMunsfayWtJjU/t9HvOmjWLrVu3MmjQIC655JLw8T59+vDyyy/z4YcfMnbsWG699Vby8/O566672n3tmJgYrrnmGq655hquuuoqBg0axCOPPALAxIkTyc/P56abbuKiiy7irbfeCi//ExEREREREekK9fXB5XdJSTbOP9/H0KE+srICrc6tzvxLi2Pe3HcwDBOHw83XX1fj8VhwOjMBJaC6gmGevI6qh4rUD8npdJKYmHjG13f73J3a80ki68jvzG63h0srRTqbni+JJj1fEm16xiSa9HxJNOn56r327DnM739fQE7OeSxYEHnFj9O5hZKSm1sds9n+yDffpPDqq++SlDSY7353Nn37Bhgxoon4eD1fHdWR1j+qgOokSj6JiIiIiIiIRI/DEdwBLykpvs15VVVLI455PP/Nrl2FJCWZjBuXR9++wQoqVUBFn7W7AxAREREREREROZWGhmACKjk5cgGI07kFl2trxHHT3EogMBjDGMSgQXnh40pARZ8qoERERERERETkrOdwBHtA2e2RE1BtVT+F5Fyxmm/O30GT1QuAYQT/SHQpASUiIiIiIiIiZ71QBVRSUlzEOTk5a8jLKyUvr5QhQw7w4pp7mfqXTBbuPD7nuYMBthjr+emeKTh8tVgsvaI1drdTAkpEREREREREupzf72fNmo9Zt66GsrK20xP19QYejxuLBRIT29eDOTY2ls9TdlAXX8nc3OPHQz8fdH3Fq2X/o+V3XUQJKBERERERERHpckVFRaxfv42tW7dSWtp2Fmjnzlj27Gni8GELDkf7d7L/PG43F6fA2NTjx8amwsUpwZ/fPrKKqiqlRrqCPmURERERERER6XJ1dXXExpo0Nbmw2dpeBldTY8Hh8GIY4Ha3LwHl8rlw0tis+ikkdKzBrKSqztOxwOW0KAElIiIiIiIiIl2upsZBYqJJWpqTPn3aTkD16RPAanWSlGTSv3/kHlAnSrAm8O20hGbVTyGhKqhkI5PzMtt3PTkzSkCdo8rKysjOzqa4uLhd8x9//HFmzZoVfp2fn8+TTz7ZqTFF45oiIiIiIiJy7nE6t1Dj+Gubc2prGwBoavLidLa9DV3fvh9x4cU7GDLEz+DB8e2OY975SRHH5g+DH+VewYABgXZfT06ftT2TNm/ezLvvvsvBgwfxeDwUFBSEx6qrq1m5ciUHDhygsrKS+fPnc9VVV4XH6+rqePHFF9mzZw8Oh4PU1FSuvvpqpk6dihFhn0OPx8OqVav4+OOPcTqdjBo1ijvvvJPMzMwzfLvR4XRuASAx8YpujkRERERERESk+9R6alm2cxmjY1bQFPDxi8IMZuXNYt7YeaTGNy9FqqmpB4IJqKYmg6YmiI1teb1ndi5jtGUFmZf4WLwjgYYvHSy8bGGL652svO4vDEyqjDg+LBngTW7YuJlbRgZjtNvtp/O2pR3aVQGVlJTEddddx2233dZizDAMxowZw3333UdGRkaLcbfbzcCBA/n3f/93XnjhBX7605+yYcMG3nrrrYj3e/HFF9m/fz//+Z//ybPPPovdbuexxx4jEDg7s5JVVUupqlra3WF0maamph51HxERERERETlztZ5apq+bzuaDy7mgj4+xqTAwvorlu5czfd10aj21zebX1YUqoDyYptmiCip0vS0Hl3NhSvB6eWkunv3y2Vavd/K5n+y/95QxD0uGQQnHY6xx15zGO5f2aFcCauzYsUyYMIH+/fu3GEtLS+P6669n5MiRWCwtL9e/f3+mTp1Kv379MAyDQYMGccUVV/DFF19EvN/HH3/MlClTSElJIT4+nlmzZnHo0CG++uqrDry1ruF0bsHl2orLtTVcCRUN5eXlzJ07l5EjRzJhwgQ2bdrUbNzn8/HEE08wfvx4Ro8ezcyZMzv0eS1atIhLL72UvLw8Jk6cyGuvvRYe27JlC4MGDWLNmjWMHz+eCy64IOJ1qqur+ed//meGDx/OpEmT2LhxY3jsiy++YMaMGVx44YWMHj2aH/zgBxw4cCA8vnDhQubPn8/ChQu54IILePjhh9sdv4iIiIiIiESH1+slEAjg97c9b9nOZRTWFDZr+h36ubCmkOW7loeP+/1+HI5GAAzDbHUZXkeu11osd293Mel9Wv2z84Tc1YnXfPJTtZWJli7vARUIBNizZw+DBw+OOMc0TUzTbPYaaJasOBWHw0FZWRllZWWnHWuPRMqgAAAgAElEQVR7nFj5FM0qqPnz5xMTE8Mnn3zC2rVrWb16dbPx3/zmN6xZs4YXX3yRHTt2cPnll3PLLbfgcDjadf1x48bxl7/8hS+//JJFixaxaNEi9u7dGx73+/1s3LiRd999l127dkW8TkFBAXfccQdffvklCxYs4I477gj3qTIMg5/85Cds376djz/+mMTERBYsWNDs/D/96U9MmjSJ3bt388gjj7T34xEREREREZEo8Hg8rFy5kiVLXuKVV3zs2RMTcW7B3gIuTqFZ0+9Qs2+AgsLj7XwaGhrw+YLf9ePjW09AdeR6rcUSSVvXfPHzFyOeJ2emXT2gOtMLL7xAQ0MDU6ZMiTjnW9/6Fm+88QbDhw/HZrNRUFCAYRi4XK523+ftt99mzZo1AKxevTriOk6v19tq5VZ7NDYGq59CgpVQH5OU1Lm9oA4fPszmzZvZunUrqanBvyX3338/s2fPxmKxYLFYWL16NfPmzSMvLy88vmrVKjZu3Mi0adMwDAPDMJq91xNf33rrreHj06ZN47e//S0ff/xxs8q2n/3sZ+H7R3L99dczceJEINiU/IUXXuD111/nxz/+MRdeeGF4XkJCAg888ACTJk3C7XaTmJiIYRiMGzeOadOmARB78uLfY6xWa7vX5cbFxWkNr0SNni+JJj1fEm16xiSa9HxJNOn56lr19fX4/X4OH66npuZV4uLmMGhQHwYMaL5rnavJRbW7modHtLzG3FxYtAuq3FXEJsRis9qoqanBYokjNhZSUkysVgOLJRm7PXBa12stlkhOrKg6+ZqVrspWrylnrksTUM8//zw7duzg//2//0diYmLEeXPnzuXFF19k8eLFBAIBJk+ezPbt2zv0P5kbbriBCRMmhF9HqgTy+Xyn3VuqsvK/Wz2WkLDmtK4XSWlpKQBZWVnhWAcOHAgEK8oCgQBlZWUMHDiw2XsZOHAgpaWlBAKBcFXZieOh14FAgMcff5w333yTiooKDMPA6XRSWVkZHrdYLAwYMOCUn9XJMeTk5FBWVkYgEODAgQP84he/YMeOHTQ0NISb0FdWVjJw4EBM02xxfmt8Pl+7K7vsdnu754p0lJ4viSY9XxJtesYkmvR8STTp+epapaWluN1eDCOG+vpytm37HQMGzKSqKoWcHD8n1nNc2dfO2NSWv5tQhVGJJ4MmVxNNNFFWdhiPJ9j3NxAIUFJST3FxKuBhyJBAh693snRbeqtJqJOrn06+Zqk3M+I1paWO5Gm6JAEVCARYsWIFX3/9NY8++ugpq2gSExO5++67w6+Li4t54YUX2uw9dDK73R7VrHio99PJQr2gOnNHvPPOOw+AkpIScnNzAcLL2kIGDBhASUlJ+HUgEKC4uJisrKxTXv/1119n1apVvPzyy+Tl5WGxWLjhhhuaLYMMVVCdyslxFRcXc/XVVwPw4IMP0r9/f9avX096ejpfffUV11xzTbP7nG41moiIiIiIiHS+YAUUXHzxRTidlTidJaxfv4qrr55JU1MGubk+rMcyC3cNjot4nbm5sI/Z4deNh0pIDjhwxdoxDKir82K3Q2VlTDgBNX+4HWg92Xjy9U42O282y3e37BHVWvXTiWOH4n4YeYKckXZ92w8EAni9Xnw+HxBctub1esOJgxNf+/1+vF4v/mPdyfx+P08//TTffPMNjzzyyCmTTxBsuF1TU4NpmpSWlvKb3/yGiRMnkp2dfbrvs9O11e+ps3tBZWVlMX78eJYsWYLD4aCiooInn2zeGG3mzJksX76c/fv34/V6eeqpp/D7/VxzzTWnvL7D4SAmJoaMjAwCgQAFBQXs2bPntGJ99913+fDDD/H7/bz++uvs3r2bqVOnAsE1vomJiaSkpFBdXc1//3fLCjIRERERERE5e9TV1eHzGaSl9WPy5HzOP38gNls9GzcWUFJSztdfW3G7obh4HQOTqiJeZ2wq3DHiUmo9tSzZtoS5xfP43xH/y+rBy/gg/gNIKMcwICUlmHxyOrfQLzZyT+fQ9SKZN3YeI9Kar9+LVP104jXvHn155AlyRtpVAfXBBx+wfPnxzOEPfvADAJ555hn69esXfg3BZti/+c1vyM/PZ+bMmRQWFrJlyxZiY2OZN29eeN6oUaNYvHgxACtWrKCysjL8uri4mN/97nfU19djt9v5zne+w80333zm77aTRKp+ColGFdSyZcv46U9/ymWXXUZmZib33nsv27ZtC4/fc889eL1e5syZg8PhYPTo0bz88svtqgKbOXMmmzdv5h//8R9JSEhgxowZjBs37rTinD17NitWrOD2228nKyuLFStWMGjQIAAeeeQRHnzwQUaMGEF2djb33HMPb7/99mndR0RERERERKKvvr4enw+SklKw22OZPn06b775Jvv3H+CDD15hwoSb8Xgy8HgeIy2t7Ws11i5n9qePUVhTCMcW2LhjXPwt5m8cds3jieHr6ds3GWhfYYenfgWkXNfqWGp8Kmsnr2X5ruUUFBZQ5a7izqFWwNfmNR2Vz2DPuvKU95aOM8wT1z/1YJF2w3M6nW32o2pNcXF+mwkogISE8eTkdG4vKAnqyO9M68MlmvR8STTp+ZJo0zMm0aTnS6JJz1fXeu655/jmm1quu+52Lr44jZQUE5/Px1tvvcXXX+/D5bKRkjKIkpJ99O2bwbXX3kZ6uoW//OV/+WpfDdOn/CtXXBFcZrdk25JWl8WF3DxgAQ+PX0xGRvO+wF9/PQrTrAcgEEhk5MivO/w+3D53s8bipgmffRaLN+Bm7IUx4WWEer46pj1tf0K6fBe8nkCJJREREREREenpTNMML8FLSkohPj5Yv2K1Wrnpppt4++23KSwspLr6awwjhqFDJ1NUZKNPHy92ezJxFgdOZwMejx2bDQo+ez587YtTgv/dVXf8fn+pWMVi/+Lw68CbL1NT9hTm1fXhYxaLk6rfXk5a1o+xTJnT7vfS2q52FRUW6uqSGdDXQ3b26W1OJu2njs8iIiIiIiIi0kJjYyM+X4DY2ATi4uKIjz8+FhMTw/e+9z0uvPBC7HaT7353AoaRRUJCAJfLICkpiZgYE6fTgcdj4PK5qDYbw+fPzW3ZELzOV4mryR1+bZkyh+rvVLaIq/o7lR1KPrUm2PjcgmlCTY1SI11BFVAiIiIiIiIi0kJdXR1+P+Hqp5M3RrdYLHz3u9/lqquuIiEhgdJSD9XVFtLTA1RW2rFaOVYBZZCSkkC6LZ1qd3WzZuAXpxyvgkqxZmLFBgSrkaqrn8WMcXMyM8ZNdfWzpKffeUbv7/LLPVRVBeOV6FOaT0RERERERLqE07kFp3NLd4ch7RRcfgd2e2p4+V1rEhISAMjODnDRRT6ysgIkJSVhtYLL1YjbHcxczc6bDTSvfDrx5+v63kIgcDzLVV0duRF5W2PtlZ0dYMyYYLwSfaqAEhERERERkU5nVh4FwMjsHz5WUflfWIyYTt0xXKInuAOeQVJSH2y2ju1flpyc3GwJHsDcoXYyXDbGph6vahqbGqyCOlx/HjdnLcDvDx6vrn6WQKC+tUsDEAjUd0oVlHSdXl8B1Us2AexR9DsTERERETn7mYWfYxZ+Tq2nliXblnDL66PwuD/B5drKiu33Uuup7e4Q5RSCCSiaNSBvL7vdfqwCqgGPJ3jMVfcbrs5qajF3wSA7PzLvxm5NPSEBdeoKp86ogpKu0+sTUBaLBZ/P191hSDv5fD4sll7/2IqIiIiInP0KP6P2q0+Zvm46y3cvZ1rW8WqWVP8bTF83XUmos1ywB5RBcnLHE1BJSUlYLOB2OwgEDCoqQhVN/hZzz0930Df5EEB4Cd6wYV8Sf94WvvthHAt3Hp+7cCdMeh+++2Ecsf3eOe33Jl2v1y/Bs9lsuN1uPB4Pxskd1eSsYpomFosFm63l9pkiIiIiInJ28X/1OU+n7aEwcLBZ02kI/mw7UMjyXctZPG5x9wXZi4R6bzU0DKOqqoq8vJHU1cVgtweIjW39nLq6evx+SE7uQ0e/hiUnJwPg9TZgmia1tY+3OX/IkPcAwhVQAPM2zsMb8LboGbVoF3gDXuZvnM+6qes6Fph0m16fgDIMI9wwTURERERERE5f4M2XMdcVAMHlNn/MLQWaN5oOmZsLvygsUAKqi1RVLcXn8/HWW9fidns4ciSOQGA0AKNGNbVoxB0IBKipcQCQnt6nxQ54pxIbG4vNZsNi8QDPYJqONuenpx/C691MIHC8P9jOip2tJi9DO+ftqNjRsaCkW2ktk4iIiIiIiHQKy5Q5GJODO525jQDVsb4WCYSQsakwML4Kt8/dclA6ldO5BZdrK01Nn5KcvA+Hw2DLls3U1IDbDdXVLVMDjY2NeDx+bLZEkpJOr3YlKSmJmBiIj3+iXfNjYn5N4FgerMZdg4kZMXkJYGJS5647rdik6ykBJSIiIiIiIp3G+NYEAGymhfQma6sJhJA7h1qxWdViI5pqPbX8vejH4dfJo/7IpynbKHeU4nB8jt9vkJ4eaHFefX39sf5PqR3eAS98r+RkRo78GxZLQ6vjMTGPkpdXyoYND7Fu3U9oaHiZQMDANCHNlsbYNpKXF6eAgUGKLeW0YpOupwSUiIiIiIiIdBpz+0eYySlsGHo9k+MGtppACLmgjy/cm+hc4HRuOafirfXU8uDG6+kXWxY+NiYtAAM/4r3Bq9hX8i45OV769w9gVh7FrDx6/Nza2tPeAS/EbrdzwQVbI46bZrAvVHx8PABNTcFquFAfqHnDkyOeOzcXLul7yWnFJd1DCSgRERERERHpVI0PLOOL+PO4boT3lHOrqpZ2QUSdo6pq6TkV77Kdy7g6o7jF8bm5UGOtYmvMe2zcuJsDB2IwCz/HLPw8PKe+vh6fzzjWgPz0ElCZmZuIjY38DAQC9VRXP0tcXBwAfr/32H+Dyb5hSa1XTkGwCurpf7z9tOKS7qEElIiIiIiIiHQay5Q5+BNSaGx0sHH9LcTaP2JN7TymfGhn0vsw+YMUfl8yn36DvyAvr5ScnDXdHXK7hPoouVxbgz/7XN0d0il9dvjFNpewFaV+zpdf/pVNm77BLPwMCj/D4/Hw6aefsXXrV9TXG9TUpLfaI6o9kpP/eMo5lVX/Ha6A8vuDFVCBgNGuRJ/V/dJpxSXdo9fvgiciIiIiIiKdK1jBUk9cHGSlZ7F4wGKmJE5hzetvkZFyIZOHTiE13t/dYbabaZqUl/9X+PWHhbN46usAJZ4MZuXNYt7YeaTGt7HWsBu4fC6mZ0XeeW5uLiyqc2LGeNm58w2cxnZMM8B/FcXg8fgA8PsTyc4eSE2Nhezsln2iTiUm5s/MWj+LI9YjPHHx8X5OO2th0a7gz//QN487jiWgfD7PsfsSTkwePnyYp54qoE+fvty5YDJ97er5dK5SAkpEREREREQ6VWOjC7/fh80WH65uycrKwma1UlFRgscTTJAkWBO6OdL2+eKLl4iL+yT8ekhSgPnD4M7tVSzfvZz3it9j7eS1WJvSiI83iY3txmCPMb072uy/NTYVrkq1sSLwPpywEeG/H1kPwMELr8F7+x34fPGtNilvj+TkZI7GHGmxE2KoAmtXHeyo2EHcwOASvEDAe+y/x+dWVlYRCEBqaiYZSUo+ncu0BE9EREREREQ6VV1dPQB2e5/wsbS0NGL7WNia9DZTN49m2P8NY8yLY1iybQm1ntruCrVd/P7ftTg2LDmYRAEorCnk11t+w7ZtcaxfH09ZWfd/1W7PErb5F6TDTbNbDtw0m6E//jEjR8Zw4YU+srJOLwHli/VhGrS6E2LomImJLyZYcXViBVTIkSOVAGRkZGLp/o9VzoB+fSIiIiIiItKpHI5g8+g+fezhY3XeOlbZXmJP+qfU+6sBqHIHK4imr5t+1iahnM4tJCTsbXVs/rDjP6/e+yJFRRZcLguHD3f/V+0+fe475Zzh2UuwXDqhxXHLZS2PnY6stKwW1U8hoSooA4O0xDQA/P5gAioQMMLzjh4NJqD69s3slJik+3T/3woREREREZEoOXlreeka9fXB3kN2e3L42LKdyzjsP9zq/MKaQpbvWt4lsXVUW5VEJ1ZBuSx1HK7YimHQrZU6oeboZWVLTjnXU78Cc/tHYE/Bcve/Ybn738Cegvm3zZ0Si8Vi4e7c+Ijjc3Phkr6XhJdptlYBVV4eTED166cE1LlOPaBERERERKTHCm0rb2T27+ZIepf6+uASvJSU40vwCvYWtHlOQWEBi8ctjmpcHRXa+a4twV5QYPFb8bk/pG/focTFpWOaYBhtntppaj21LNu5jFf2vkKVu4oMWwYj3SPJ+mY+1/7jbK65ZggJCeD2ubFZbc3ODex4Gcujz2Aca+5tGXER5l//3ClxOZ1bGJXqiTg+NhUuG3I7jsPBBFRTU/MElMvloqHBidUaT1qaHTA7JS7pHqqAEhERERGRnuvY1vKtcTq3cLRyGTWOv3ZxUD2fwxGsgEpNDS7Bc/lcVLur2zynyl2F2+duc05Xa08fpVAVVIzFoE8fL5988hZeb4D6+q7JPtV6apm+bjrLdy+nyl0FBD/LzWxmQ85qBgxJJ+FYr/eTk08AlilzwsknAMOegmXKLZ0SW3s+P5zPExcXbELu84WakAc/u8rKSvz+YAPy+PguyuZJ1CgBJSIiIiIiPZa593PMvZ83O1brqeXhzQ/z5y9mUlr+S9Z/+QOGPTeMh7c8fNb2ITrXNDQEE1ApKcEEVII1gXRbepvnZNgyWk2QdKecnDUkJX3C2nUL2NnGozF/GDQZTSTYE2hsPMwXX3xMTU3XfN1etnMZhTWFrY7VxJbzRtWyLomjNTk5a3i15l4mvU/EP6uOjgsnoE6ugDoxARUbq+qnc50SUCIiIiIi0qME3nwZ/51T8N85BarKoao8/HrXU08y4f8msb30OcakmCRbg8uA8pJdPPfFc3z/ze/3+iTU3r0x/PnPNnbutOJytT3X6dyC07mlxfFQBVQoAQUwO6+V3dZOMHtE2+PdxeFwcF7GkVYbaYcMS4axKXBwyEFIcPH551spKjqKzxf9+E61tHFtUdvj0daepZfHe0AFK+BCCaiKigocDgsOR3+qqpS+ONfpNygiIiIiIj2KZcocjMktkxnl427l5/GfURNT3mJb+NDrfbX7ztpm2NHkdG4JL0XcuzcWl8vgm29i+frrWAoLrRw9asHTSiufqqqlLZZZ+f1+nM4GDMMgJeV4E/J5Y+cxIm1Eq/fPiR/CvRff23lvqBPV19eTl9d2HyiAecPgDwf+wFv93sQf18hHH71Febn/lOedifYsbazxdt/SxvYuvQx1pz6+BA/Ky8vZuXMPjY0G6enZOBxagneuUwJKRERERER6HONbLbeRT/+n8eyO+aDVbeFDW8JDsCKjt6j11LJk2xLe3XML67/8ARe9MIZXqh/lm7J6kpMDxMSYeDwGR4/GUFgYy9dfW6mosNDUdLxBt8u1tVkVlMPRQCBgkpCQRGxsTPh4anwqayevZd7F88iwZQBgt/RhdPU47or9GanxbZQYdaP6+noKC8e3e36Jt4QD2Z/hcFSzYcOHUYysfUsb0+K6b2lje5de2hOClXJer+fYf/28/fbb1NUFGDVqLEOG9Kdv30DU45XoUgJKRERERER6nNDW8l9c8RCfXvIzAkkp+Hb+FSfOFtVPIaHjZ2Mz7GgINa/efHA5F/TxMTYVcmxVbPQ8TUHitXgtX5CaepC4uAO43QcpLz9IUdEh/v73YjZtOkJJyfHKpxOroKpLnmDsBR+SlGRvcc/U+FQWj1vM7h/uZv+P9rPuqre4pOoqqkvrMM/SFj8Oh4MRI05dARVqRg7wpe0zPB4rH3ywi88/b+zwPU+sSDuVUy1tnDOqe5c2tmfpZWgJXlOTB4tlM3v3/i8lJZXYbBlcc82VXHmll6wsJaDOdUpAiYiIiIj0MH6/n7KyIzR2/Htvj2I+/Az7UiZyeMBV7JvzW0y3j3F94iL28glVQZ2NzbCjIdS8+sSEXOjnI4F9LN/9AAUFBbz1VgEffriKnTtX8emnq/joo1f4/PP/BI4nZVyurRRVvMZNr99EreVZRl62jSdTHuCm12/iYP3BVu9vs9rIycnCajWorDyKwxHd5Wqnq66ujg8+mElZ2WeY5lFyhu5j0vu02pQ89PnVeGtIsPfFNGHfvlP3FAslnE6uSBvz4hiWbFvSZl+yeWPnkR2b3erYiLQR3b60sa2ll6H44uLiMAwDv7+JQOA/SE39PxobY7jiiu8xcKCVmJhWT5dzjBJQIiIiIiI9zMcff8zSpat4/vlivv66d35zs0yZg5mcQkpKAIsFUgbaKRn7j9w1OK7N8+bmnr3NsDtbwd6CFssRT1yKuM++h6ysLLKyssjOziYnZyDDhg3koosGcsUV21pc729FCxgat4NkKyRbYcZAkx0VO5j46sTISSibjX790gkE/Bw4UB6Nt3nGHA4HpgmJiXZiYkwSrAlc2dfeaiLzxCRmzoAMDANMM3LyqLWE04kVaQPjq1i+eznT102PmIRKjU/l3oR7uaTxMuyWNABSrJn8cMh81k5e2+1LG09cepkaG1x6mRqbwbyL54XjMwyDuLg4MjOLsdn+zoABxYwbZyc7ewAZGap86ims3R2AiIiIiIh0roMHD9LQYFBTU0pZ2fkMH352VpZ0hb59AwwYYJKVFeDQoU2cn97Q5vyxqTCx/6VdFF33CTWHfriVwpS5ubBoFzTSyLSbp7WoBnM6t1BS0jKhdHGqSd4Jq+7m5sIfS8Eb8DJ/43zWTV3XaiyDBw+grKyKoqIyxowZcAbvqvO53W68Xi8WSyzx8QlYrcF1gvOH2wFHq+fMzYV9zGZIpZ2DBwNATavzQksgbYFCbh4bPHZhH3+LirRFu6CwppDlu5azeNziVq/lqnZxae1VLB73MnGJ8cRZbGRn+0mNPzuSN6GllzPTH6a2wUtqchzDhjX//1JcXFyzpY4jRrxN//53YKj3eI+hCigRERERkR7E7/dTXl5OXBw4ndUkJZ2ljXW6gGlCRYWFffuslJVZiI9vX3NxT/2KKEfW/dpbxdPaUsSTd71rdt0TCu6SrTDj2MqwHRU7Ip4zZEgWAMXFZe0Lvgs5HMEkU3JyCoZhYLVCw4b/oF9s5FjHpsIdIy4lI6MPADU19a3Oa20J5PxhkSvSIjXHdzqdOJ1OLJZ4kpL6UFeVyL59Vurqzr7MjWFAnMWGtZVSmMzMEjIzS8KvY2K2Eh+/uQujk2hTAkpEREREpAeprKzE5/Njs5kkJVWRktK7E1B1dcGvPNXVFrZv/wHr1v2EjIzd5OWVkpdXSln5S/zuhYW8//4fGDCgjLy8UnJy1nRz5F1j3rCkiGORliKGdr5rr1ByxcSkzl3X6pzBg7OwWKC8vAyn8+x6XuvqgjEnHNulLSbGpGbErlOe56lfQWZmMHNUW9v6+25tCeSw5JbzTtUcv7KyEoDk5EwMw6CuzoJhmDQ2np0JKKDVnk65ue+3ONZWslPOPVqCJyIiIiLSgxw9epTAsVU3DkctbvfZ9YW+q6WkBHA6Dex2N3V1dVgsFlJTj3/jHzlsJJusf6e0dD8NDVdjb7lxW4/kdG6hf9yRiOORliJ2NCEQqoJaW2qQYktpdU5qaip9+iRQW+vk8OE6zj+/e3sWnShUAZWYGKxmslphwIA1mKWHCPz7/PA8txEg8d+XY2QNCh+rrXUc+2/LBFRbSyBPFqqCKvG0XpFWWVmJaYLd3hfDgDFjvNTWWs7K3knHE1DN/7/kdG4hOXlfi/ku11aczi0kJl7RFeFJlCkBJSIiIr2S07kFj99Dmn1Sd4ci0qmOHDmC/1hrFb/fh8PhwOtNJK7t3ts9Vt++AeLiTOLiKgBIT08n5oTyi/79+5OWlkRpqYNDhyoZMCCju0LtUpWVj59yzt6Sh4izjWvWxDpSddiuL3NIiGk94TE3F4q8l0S8j2EYDB6cTW3tPoqKDp+VCaiEhD54Ai5iYmIBMLd/BPYULHPuBsD28v9i/m0zxpTjCaiUlGSs1hjcbhcNDR6Sk+PDY8eXQLbeR+pkob5SrQlWPUJqaiZxceax3kpnZ9+3UALq5CV4bSU2q6qWKgHVQygBJSIiIr1KraeWZTuXMTpmBU0BH78ozGBW3izmjZ3X7TsFiXSGo0eP4veD1RqHz+fF4ajB40kiLq53VkJZLJtpCripqkoHIDMzs9m4YRiMGDGE0tIv2LfvGy67LANLL2hU4nL9kj/96U8YRl/GfWc6i7+eQrKlkCePNcNeuBN21ZUx4svpp9xJrbr62YjJJwhWQT05bkKb8eTmnseuXfs4dKgMGHU6bykqymrK+CD+A56tfA5HeS3puzOYPWIW9wT6k/boMxj2YFWXZcRFmH/9c7NzDcMgJSWFqqpqqqsdzRJQ0HYj85O11Ry/qqoqnICy2c7uv+cVFRaKi61YrSb9+gWfmVMt61QVVM/RC/7XKiIiIhIU2nGoo1tci5wrfD4flZWVBAIWsrPPB6C+vgaP5+zrBRNttZ5aHtu+hL2um/my4RZu/uRmPoj/AFtqyyVMw4YNxWqFkpL9OJ2947PatWsXpgmDBo3l1cP/w35HYYvd1+D47mttqa4+9bI80/n7NseHDMnGMODw4VK83lNerkvUemr5dcWv+Vv833AEgv8+VHuC/2bk+16m7oSkrmFPwTLllhbXSE0NLt2rqGj+74vTuaXNRuataa05vmmaxyqgDFJTM4mPP7sTUKG/Xyf+PWvPsk71guoZlIASERGRXqO1HYc68iVLpL2czi3UOP7a5fctLy8nEAjQp08GaWl9iYmBhobel4AKJZu3lSznwpRgsnlIcj1/i/8bjxx8pEWyefDgwdhsFqqqDlNR4eymqLtOdXU1xcXFBAJx5OZeyPrKVS2aYbdn97WQYcO+pN/gL/jX3SNYuPP48YU7YdL78K+7R5CZ03bj8n79+mGzxUoVTLUAACAASURBVFBXV0VVVctG291h2c5lHDWPtjrW3n8z0tKCH2J1dfNKp/YkVBqdo1i58gF2734pYnP8+vp6vF4vsbGJ2GxJZ30F1PDhPoYNayIr6/gSwZycNeFNASL96S0bA/R0SkCJiIhIr9HajkMd+ZIlciq1nlqWbFvCu3tuYf2XP2DMi2P4j4+XUOXsmuq6o0eDX5ZTU8+jT5904uJM6utrcJ8d3+e7TFvJ5qLGohaJg9jYWIYODfbuKSws6pogu9GuXcFd3LKzR2NaA9Q1VTX7rEJOtfvaic40wW+1WsnO7g9AUVHk5uhd6VT/JrTn34yMjOA/MDU1zRuR5+SsYeDAVyOeN3Dgq8RYngOgtDRypdTxHfD6Apz1FVBZWQHGjPGRlXX2NUiX6FMCSkRERHqFRm8j1e7qM/6SJRJJpCWev/1sOd/9/+zdeXwc9X34/9fsfWpXu7oPS8Ky5NvCECAEBwdIiAMYYiA45ChNk7TBNA+Sb/tt6u+vzbffxm1CW75NW5vEyTcpoQEnoQ6JQ8JlCARfYGP59tqyjXzo1mpXK+29M78/xqNrV9LK1q3P8/Hww9qd2dnR7Gi189b72HY/Jxu7J3wfBgagnE53XwBqrmVAXUmwedGiawDo7Pwt/u7Jz16bLIlEgmPHjqEoUFZWh1mnNcNOX1c7Zl5L5ulrA41HgL+ysgSAzs5XCYd3Z/09TYRIMoI/5h9xnWx+Z3g8agme358+CW+0xtvz5hUjSTra29uJD1OX2NnZiaKoAShJAsvIL5MgTCkRgBIEQRAEYdZTFIXdb+7mA07TVV9kCcJwRsoAaU6d5HtHNk/4PrS0qJkjbncxTmcuJhP09gaIx5W+yXiznTbefqzB5oIKD/X5b2Ku/i6vnVSz1zbt2zTresP5fD5isRhudzG5uUVYrcrlZtiZPVIJ62szT1/TXOkxH+qaa4qRJPB6/4uOjqnt+WM1WHHoHCOuk83vDK9XDUAFAoMD0Nk03tbr38XjKSSRkGlubkbpaEXpGFwSqE3Ay83Nx2RS+qbMCcJ0JAJQgiAIgiDMevv372ffvsN8aZ552HWyucgShJGMlgHyWudzE/r88Xgcv98P6HC5CjCZDLhcTnQ6md7e4JzJguofb5++bLhgcyAW4I9+/0cYKt5lea48qwcUHD58GICKCnXcnc329ojNsOvc8MXazNPXNFdyzDMpLS2ltPQ8eXkNRKN7pjQLKpVKsTS+dMR1svmdkZ+vvgEEg0Fkub88LpseUIHAkxQWlqIo0Nh4EcV3FMV3dNA6WgDK5Zr+E/AEQQSgBEEQBEGY1Xw+Hzt3voXdfpEFecOPu87mIksQhpNNBkhXfGJLPNva2lAU5XLzcQMmE7jdbvR6hXj8NToCs7esbKixZvSM1r/oO7ueGvd9nAp+v5/m5mZMJjOwhIYGA+HwP4/6uEzT14a62iwqAKvVygc+8G7f7amcfNbY2MjK8ErylcKMy2tza3l0xaOjbsdqtWCxmEmlEgQC/Q3utcbbeXlHeOGFr7Nt27dIpVrTGm+XlZUCsG9fC81vHiN8sD8AJcsynZ2dJJMSLpd32vd/EgTDVO+AIAiCIAjCRLl06RIvvvg7AgEdDz54fNT1Y91bwfWxSdgzYbbpzwBJD3JqGSAXohNb4qmV33k86gWz0ahgyjFxIPct6uz/SvvFFHfv9PJQzUNsqNuA25whXWUWGG28fZ0bVhcODjaPlL12KAi/anyOf+SvJ2qXJ412jhQVlRMKWTAYFGKxF1i6NDlovWgyOqZz9UqO+XDb8Xr7m8BHImoWlM12c9b7Ml5OnTqFBQtfd/8L79n2sdP/LP5YJ16Ll/W163l0xaNZ/wy53S5aWtrw+7vxeOyDlkUiEWQZzGYren36Y02mMvx+PU1NzXzZfQhadcgy6HQQCASQZRmr1Y3JZMZiSaZvQBCmERGAEgRBEARhVurq6uKFF35FZ6fC/Pl1uFz/g/nzZSRJnQD12muv4XAv5v7AaVw5MsYvPT7VuyzMcGoGSOYsu0cq4VhsYks8teCC11sMQFQK8J3275Bbdp5ll6+TtbKynRd2sv2e7bMyCJXVePvAlr5gs5a99je16es9UglfOwTBZOeYgzLTUVtbGwBOZxFGozqFzONJn0Y21u8zm2OeTYA/03ba2p6ksnJyA1DJZJKGhgYSCagqvo6VuR/lyU98g7h8ZedAbq4agOro6Ka6unjQskgkgqKA1WpDp0vPYCo58ALfl14DKxBT71P+dC0pIHXzRwFwOPKA6T8BTxBECZ4gCIIgCLNOJBLhl7/8Ja2tUfLzq7j55tuprJT7mrOazWovKAMy9kuH4fTREbYmCKPLJgPk81U3TOg+aBPwXK4iAP7r3L9zPnp+2LKyLYe2TOj+TJXy8uf5RdejfORNqB/Quqk+AB95U/33XGv/a5FN/yKXYXYMKNDOEbu9iPx8mWuvjVNSkh6AGiutnGzgv3nXnEkrJxvJcE254/E9+P3DN+ueCI2NjcRiMWy2AlwuL7m5Mjrd2ANzGo9H7QPV2ZneS0wLQFkstowZUN23PsyhsvvS7pfuWc/p2uvEBDxhRhEBKEEQBEEQZpVkMsmvfvUrLl4MYLUW8uEP38M118gYBuR9F+x7ncdPvskje7+PKdSGrquN1JfWkvrSWuRfPzt1Oy/MWNlkgMRD/9+EPX8kEiEQCKDX6/uyIX51/rkRm6Jv822bsP2ZaqM1hB/6vY/Wv+iesk+P6fkjkQi/+MUvePvt/ezaZeKdd4w0NU3tpZeiKH0ZUBaLGqR0OCYuY2Y8s6guXfpHIpHJa6J/6tQpFAXy8xcB4PFc3XHKzc2ht1fi2LHetPMgHA6PWIJXWKgQW7Ym7f7oklW0t7eTTILbnScm4AkzgghACYIgCIIwayiKwksvvcTZs5eQ5RxWr76fa64xYLUOXi9yx73s9VakPV66Zz26tQ9P0t4Ks0lx8TZee+1/8ZOf/A8uXKinsLCJ4vKTPPvbP+W+3xTTk4SIcoGyH5Rx9wt309jdOK7PrwUWCgoKkGUDMTlCV3zkpuid0Yltij5VsmkIP/B7zyZ77StLxzag4J133uH8+fO8+upemppkAgEdfv/UXnp1dXURj8cxmeyYzQ61R5hpSnepz3DZTxqz+QAnTrxBdBJOV638LhqF8vJarFYFq/XqAlAul4NYTKK3tzftPAiHwyiKNGwGVCgkkXP6bSIGJz8yLGVH8UqSVhfxPbsvNyAXE/CEmUMEoARBEARBmDXefvttjh3z0dtrZvXqdVRV2XC70z+UWywWTuXkp90vXX/LZOymMAs1NDQQiUSw2wvIyyvGZlPwJ/z82P7/uGNhMw4DOAywrlThYPtBVv9i9bgGobTSqsLCQuJxMOtGLyvzWmZHWdlQ2ZTUDfzes8leS4W/n/Xz9/T0cPDgQeJxiEQSRCINRKNSxl5Lk0kLUrrdag8iu336BCyyeQ0ikb/l1CmFWGxi9+X9998nHo9jsxWRk+MZl9fNYjFhNiskk/G07Q0swZOk9NdEpwOLWeGND/8/2soW4rM72fmhxwmHZfz+ALKsFxPwhBlDNCEXBEEQBGFWOHLkCHv3vkN3t4FVq+6lsjKfoqJUxnXNZjM13e2E9SY6P/oX2Gwy3lefQtm/C2ntvEnec2E2OHz4MIkEVFfXYTKB0QgbXtxAkmRaD6b/vgRxOc5jrz/Gjvt2jMvzNzc3A1BmNSP725BchaM2RW9gYpuiT6WxfO+ZehNpDcefeGIzHR1xvvzlR7N+7r1795JKpUgkLECcYPAYdXULyMvLPpDR2dmNLEN+fk7WjxmNFoByOAov/z99AhYj9YeKRCI888x/0dgYoqBgHxcurGbVKomcIYcmkoxgNVgzb2QMfD4fySSUly9Ep1Nwu68+AGU2G7DbFXJzY2k9t/pL8DJnQOl0CtGP9lAi/xDPvI+wd+9LHL9wHNv1dxF9+z8pL+/CaNyHxXLjVe+nIEw0kQElCIIgCMKM5zvj49VXXyMYlFi58qNcc00V8+ZlDj5BfxPyH8+7ke4Fq+hduArd3/3HZO2uMMv4/X4uXLgAGKmqWoTNpl7Y17fXc3+pmvmkcRjg/lL164PtB8dtH7QMqPyuTuyXDmMwvD1qWdkXa8dWVjZTZFNSN9r3rmVHuVx2APz+3qyeOxgMcuTIEZJJiRtuuBedTqK9/QzxeJRwOLsGPbFYjCee2MY3v7mNc+eGfx8bK21KotWq9n+y2aY2IytbVquVu+++C7db4cSJAwQCPVw81ELgfCNt3QE27dvE8meWU/3japY/s5xN+zYRiKU3+85GIpHgzJkzRKMSFRW15OQoGYNCY2WxqL9z4vF42jK1BC9zE/LG7kb+7MAaYtLfIOn/kYeO38k25zba4qc4fvwdIhGJxYvfwmD4J5EBJcwIIgAlCIIgCMKMFIipFx5Ln17Kba/fxmbbZs5UNVBaPY+KiiS6ET7lGAwG3im8hl5JRyqVRJZBcrrQrR1bo2FBADX7DqC8fBEmkwWbTaEr2oWCMmIfIgWFYDR41c8fDocJhUIYjUZsF89hu3gYvf6fRn1crHvrVT/3dJRNOVfQ/29ZbUsLQAUCPVmtv2fPHmRZprh4MSUlVVRVlWIwJLl4sYHe3uwCUEePnqSzM0IsFuHddy9m9ZjRaA3I1X5BRRgMyoyamFZSUkJNTTWW3G6ebftzHjx5HUtevpnrfracLYe30BntBNTeXlsOb2HdjnVXFIRSy+8S2GxFOJ2541Y2abEYAYjF0gNQA0vwdLr+IFJjdyOrf7Gaxc6Dg0p4m6QmtuU9zdFLe8jLO09u7il0ut3I8u5x2VdBmEgiACUIgiAIwowTiAVYt2MdWw5voSveBUBUH+Et+Vd8w7eWCCNfeEiShMViQZIgHo8iy2J0kDAypaMVpaM17f5kMsmxY8cAqKxcAai9dXItuWnZTxotC0pCwmVxXfW+aZkthYWF6M8cxX7pMBbLL6mpudT378iRf+OXO75KS8vrRKNtOBzNI5Y9zWTl5c/z9ePX8pE3oSfZf39PEj7ypvrv64ey+5l3u51IEvT09JJMjryu3+/n+PHjKIqO6mq1n9zKlTUYjQrvv3+CcHj0Sy9FUdi//zBmszrRrLnZl9V+jiYYDBKLxTAaHdhsjmnV/ylbyz6wjNcrnqPe8WsCdLPCBctcmTPEfF0+thzaMubn8Pl8xONQVrYQk0kZtzJFs1kNQCUSiUH3K4rSlwE1dArehtc3EJfjaSW8ACkpxe7i37ByZX/Qye8fPfAqCFNNBKAEQRAEQZhxNtdvxteV+cLsdDC7Cw+z2Xw5ABVDnhmVKMIUUnxHUXxHB92XSqV47733iEQiuFwFRCJlNDQY6OpSgxt/UjX8R+1HKuHa/GvHZd/0v/sFj598kwde+ym6rjZMoTZK//4uUl9ai/zrZwGYN28eBgwEAucB6OmZ3ZcB9e0Hx6X80W63o9dDONxDLDZy0GrXrl0oikJZ2TIcjlzcbpmlS2swm3W0tDTS1RUmNUpFXXNzM83N7bhcRjwe+XL53tWX4Wn9n5zOQiRJmlb9n7L13IXn6NR39t1+pJKMGYaaZ09sG9P2E4kEZ8+eJRKRqKhYSG7u+P1iMJvVcYPJZJxUqv/Yx+NxZFlGrzdhMBgHBaBGK+EtKWihuLg/Qy4S2UM4LLKghOltdv/mEQRBEARhVtp2auQLi22+0S88BgagRrsoFAR8R8B3BEVRaG1tZefO1/m3f/s+L774Nu3tOtzulXR36zGbZbq6dPj9P8CqH/4C1mGAf71hfKYuvu0uY6+3Iu1+6Z716NY+DEBZWRkAbW3n1abGUYkhyRizhlr+mDk4MdbyR4fDgcGgEA6HRgxAtbW1cerUKSRJT1XVhwDIz5exWq1UVlag18s0Np4atQ+U1sy+pqYOtzuPeDzKyZNXX4anZcnZ7WoD8pnS/2mgbUee7vt6hUvt5aVNNcykK95JNBnNevvnzp0jFkvgdJbgcLjGdWqhTqfDaFQjSbFY/w9eOBwGwGRSm6drAahsSngzLcum/FQQppIIQAmCIAiCMKNEkhH8Uf+I63RGR7/w0AJQiURUZEAJo5J9h4keOcDWrc+wZctPeeWVQzQ3xzCb87nuujtYvHg5lZUJiotTeDxyVuUwSvg/r3q/mpqaaGlp4f28srRl0vX9Aa7CwkJMJhPBYABFUQMvodDsLD0dz/JHp9OJXg+RSA/REd5Sdu3aBUBlZR1Waw4Oh4zVqma61NbWYjQqNDaeHLEPVCQS4eTJkySTEgsWrGDp0moAjh5tGHU/R9PW1kYqBTk5Rej1CtarHxY3qSLJCH6lvxF8prK0odxGb18z+Wz4fD4iEYl582pxOmWMxivb1+EYjWoWVCTS3wdKC0CZzTaAvh5Qo53DX61Wg29DiSwoYbrLcEoLgiAIgiBMX1aDlVxTbl/vp0y8ltEvPAaX4M3OC3Hh6si/fhZlh5pNJwFm4Iv71b5J9aUfJPrRL1NVlYfbDTbb0DS6E4NuvfTSS7zy1kFuXLGGe++tG7cG0O+99x4A16OQtLq49IlbSSgJ5r+yG2X/LqS18wA1A6OsrIyzZ8/S3X2eaNRDa6uJJUsSaWPhZwO1/DHz9/VIJZyLZ1f+aLfbMRgUIpEe4nH1fSIc3k0sFSPX+RFADQKePXsWg8HIvHkfBKCgoP+5q6ursVpfo739Im1tPRQV2dKep7e3l1deeY1AQMZun09HRx6lpdXAXny+M6RSH0Gvv7LcAa0BeTwOXm/RjOz/ZDVY8Vg8+KP+vuwnjZYFdWhIQtvdpfdlvf14PM7Zs2eJRtUA1HhmP2nMZhPhcHhQI/JIJAKAyaSeEwNL8EY6h9eWDP88nZ1PYrPdfNX7KwgTQWRACYIgCIIw43y88OMjLl9fu37UbfRnQMUARBaUkEa39mEuLL+ZSHmI4Lz+q9ueWx9i8V/9NR/6kJeSEgWbbfQL+vLycux6M83NjVk1o85GKBTi9OnT6HQ6lBw337htPvXev+eQ91usvLaefwj9ZtAksOLiYgB6e9sJBnXEYhJ+/+y7HBjP8keHw3G5B1SIjp4gm/Zt4uXjn+bVE59l+TPL2bRvE6/84RUAqquvw2RyYLMNbl5tNpupra1CURQuXPgF/u43+pYpisKRI0fZvPlp9u49SyxmoqTkFlwuGbO5CLc7h0ikl4aG5is8Gup5EolE0Ott2GzOGRmAAlhfo76vj1SWpnHFvKw23pX1ts+dO0ckksTtLsHlyiEnZ/yPkcmkZkDFYv3d7IcLQI12DutH+JuJyIISpjORASUIgiAIwozziZxP8ErqlUENaTW1ubU8uuLRUbdhuZyCkrxcqifLoJt91+LCVTjffp6/Vv7Ap+48RUKCb+038GC7l8dWrRhzCdO8efMwmaCt7SLBoIzHc/X7V19fjyzLFMwr4wuxrbjjJ/nK5aqyMoufp4K/5/Ud69h+z3bcZjcul7qwtzeIyyXT2ytNSKbHVDl37hy//W0Dt9/+L6P+LKvlj3816jbtdjtGo0Qw7uer9Wtxm3w8WKcuKzN3suXwFrwpL58zfY7S0hsAyMtLbypXvqCU+tM/4oa8A7x2UuZbPi9r562l9OwCzp/yk0pBSUkVt956Ozk5biIRGY9HYeHCavbufY+jR89SW1s6xiOiam1Vpzc6HEUztgE5wIa6DZy/+Bx17vTsVy0L6nzEy83W1VjfKybgDWW9ba38bvHiWnJzZaQJSIo1mdRL72h0cAmeOgHPhiTR97xXO9FOZEEJ05UIQAmCIAiCMOMke5I81PsQ75e1syv8BsFkB16Ll/W163l0xaO4zRmaYwxhNpsBtQcUQCoFBvHJSLgsEAtw/2/uJ7+oiSW56n1leUm+Z2zl9Z2f55efeT2r80zjdDopKMjlzJkA58+3UlVVeFX7l0gkOHLkCAD7TPU0Bk7yeE3/8kcq4WuH+sfRb7xhIzk5OQD09HQzf75MYaEyq8rvfv/73Rw82IHP97+oqKikq+tdiosXs379JzDYu7Lq+TSUTqfDbrdxQn6Lxohv0DF+rBq+dAA69Z2cym3kTp0Nk0nB5Roc4AnEAvzF8b/AWnGK5dq5ZO7kx6d+jCvm5W7jH7N61Se4+eZa3G6A/gDWihVqAOrEidMoyi1IVxAZaW1tvdz/qRCdTsFimZkBKLfZzTeuryYRezfj8qduupGKedvx+Zr5f/u3cenSpay2q5bfnZvQ8jsAk0n9nTO0BE+WwWKx9fV/AqiuPpH2+GA0eEXnsCBMJ+LvfIIgCIIgzDhdXV1YsPBHpf+Dbdcd5+D9Zzn8ucNsvGFj1kEBLQCVSk2PErxweDddoTdGX1GYFN9++9s0JZsyNjs+lWply6EtY97m/PnlSBJcvHiBeDx9uSyT9WQ6NWMjQn5+ITv9Lw7bFwf6p0IODECpzyehzMxYRJpkMklLSztms0I47Ofs2QPE4xKVlQtxOuWrunB3OBw0OI+mHeNqR/8xfrv394Da+2lojGhz/WZOBU5lPJeC5k70t4ZYs0YLPg12zTUl2O12uruDnD/fcUX7r/V/8ngKsdmUCcnumQzh8O5hg08Aseg+wuHdlJYWUFp6CaOxvq/EbSTnD+5HH+ghL6+U/HzHuPVnG8psVruaD52Cp2ZAWQf1f8pEBJ+E2UAEoARBEARBmHG6utQSDLtdTSewm8d+xaD14+jvATU1V2WBWCBjX5mBvXuEybf93PasgjpjMW/ePIxGhZaWxozT0N55x8iOHVbOnRv5I7qiKBw4cACAssqldCc7R+yLo02FtNvt6HS6yxe9agQsmUx/3EzU3t5OIiFTWOhiyZJ8nE6FeNxKMllNR8fVXfKYHWZi+kjGY/yYOqiO7lQXQbmF3Nz0SPa2U9tGPJdebHp+2KCQJEnU1s4H4PDhsU/DUxSF1tZWEgkJj6doxpbfgVpWls06VqueD3zgXerqdmeVBRV8dxdF3YHL2U8Td3z6e0ANzoBSFLBY7KMGoARhNhABKEEQBEEQZpRYLEZvby86nQ6zWc3o0OvHftEwtARvKjKgArEA63asY1fjFpbkJKlz9/eVWbdjnQhCTZFwIkyv0ptVUGcsysvLMZsl2tsvEQym9wlqajJgs71NU9O+Ebdz8eJFOjo6sNnsFHrruNmTk3Ekuxbk0KZC6nQ6nE4nALGYmgWVSt+NGamlpYVUCvLzS3nwwQe45ZbFLFt2OwUFV99oPdeRywdyTBmP8cAsqJe6tqYFkiLJCP6o/6rOpeXL1ShXW9vLY24u3dPTQzgcRpIsOByuGduAHKC8/Hlqai5RU3OJ667r7vt64L/y8ueJxXaTl9dAcfFFWltfG3GbsVgM47nTVMSCVFbW4HZP3C+C0TKgBpbgCcJsJQJQgiAIgiDMKIGAGpTJzc1FltWPMkbj2LejNSGfyil4m+s34+vyZSzN0Xr3jERRFN5//yLvv++fNaVU04Eck7lhmIDD0KDOWFitVkpK8pHlFOfONaUtd7lkCgu/g9v9xIjbee+99wBYsGAFqZSRDdWOYdd9pHLwVEitDC8c1gJQM7Qea4jW1lZkGbzeIhwOM3fffSdr1tTidCpX3dPH4XDw52XDp6doWVA7Lj6XtsxqsLIq33lV51JNTRlms5nq6t/R3Pydvvt7m1/Ef+mXI+67dlyczkL0erKa2DjTDcyUMpmeHXHds2fPUtrbRbUcorTUMaGDKDJlQIXD4b4eUCIDSpgLRKtNQRAEQRBmFC0A5XbnIssSksQVfXDXMqCSSTUANZ6ZIEqHOnVKyhu50fRIpTmHgmqZ18YbNqY9LhwO8957x9i79whtbUGSSTuf/OQKisujVFfeOn7fyBzV0dHBlytMQIZGTahBnQbWZ1w2mvnzyzl1qp3z5y+QTJYRa3+RmBzHU/pJCgv/gMm0C1D73WSaYhUIBDhz5gw6nY7i4mtRlF2UmNKDWZo6N6wuvL7vtsvl4sKFC0Qi6s/R0BK8cHg3sVSMXOdHruj7mypqBpSE11vM5et8SkrkcWmy7nSewWMcvpeQlgV1KKhmMg0NJj22wAlknsiWzblkMOi57ro4xcUXSaUu0hZ8hR+cfJfFuu+TUFJ86/Vv8lDNQ2yo25DWA0/r/1RSUojVOnP7P2UrHN5NJLKn77bLdYZQ6A84nasGrSf/+lmUHdsoLw9BCVgvOHH93V2kAOme9ejWPjzu+9afAaW+ryiKMqAETwSghLkhqwDUrl27ePnll2lsbCQWi7FtW3/Nu9/v54c//CHvv/8+HR0dPPbYY3z4wx/uWx4MBnnmmWc4fvw4oVAIt9vNbbfdxn333TfsFIdoNMrTTz/N/v37icfjlJaW8vDDD7N06dKr/HYFQRAEQZjptP5PDod6oXUl5XeQHoBSFAkYn+wAxXcUGDkApZXm/E1t+jJtgplWmmMxWFAUhUuXmti9+xBHjpwmFlMvrGO6KPvsf+Bm0yaa25Ks+4N32ItRITtdXW8w39Mz7PKhQZ2xmDevHIPhAO+3nuD/7H6da639QYTvLjNTejl40t7+JBUV6QGo+vp6FEWhunoRqZQTk+mfRn3OWPdWcH0M6M+AikTUgIgWeA3EAmyu38xi/VYScpJv+WbOeRSLxfD7/ciyHo+nAKNxfLN8TKbnRs0wfKwavnEsPZMpHN5NgTH7AOFwamtf7fv6nTOPsqsxwoN16m2tbHfnhZ1sv2f7oNertbWVeFy6nBk2t7KfNK2tT6QFoHRrHyaRTOHP+RYApT9T34gnKvgEYLGov3Pi8cTl/+OkUin0eiMGgxGdbpbUwwrCCLIKQNntdj72sY8Rj8fZunXroGWSJLF8+XLWrl3Ld7/73bTHRqNRysrK+NSnPkV+fj4XLlzgO9/5Dkajkbvvvjvj8/3sZz/j9OnTfOc738HtdvPyyy/zxBNPsGXLFhyO4VOMBUEQBEGY/bQAlMvlojb2EwAAIABJREFUAcBwhfncWgAqHld7r4xrLxzfEfX/D90+7Cr9pTnpmRFaFtTFmBcpJXHgUD179hzh4sUOZFn9/FVWVsXCa6v4hu8LFOW0DBrvPtzFqJAdne6ZUdcZGNQZi7KyMiRrnJ/ZvktlSwefvRxEWO3tpNQ2YPuxPXSF3hiUiRSPxzl6VA1uVlRcB4Ddvp3y8sEnb6YsHI3WAyocVjOgUimJQKyLdTvWYZF9WQU1ppu2tjZSKQW3Ox+DwYDRmOUowSzl5v4nTz/9NMtv2kJFfuZeTT1JuG/+fWn3Z9M4e7RzKRzejV5f33e7xhHpK/uD/oC1VrY7MGuyvwF54Yzu/5SNodlPGll+L2NG4fsl3SiXA82R8hDWC06k62+ZsP0zmdRfVloAKhwOA2A2qz/4E1n+JwjTRVYf2erq1N9Ex44dS1uWm5vLxz/+cQB0GX5qCgsLue++/jfjefPmcfPNN3Ps2LFhA1AtLS2sXLkSj0f9YHn77bfz4x//mNbWVhGAEgRBEIQ5TivBczhySSTAYLiyiyqj0YhOpyOVSpJKJcd1Cp5y6mhW622otjNSac7LZ27iiSd+QCikXrBYLDYWLVrGTTctY948J9/ev4lWpYVvVA5+3HAXo0J2jhz5Aq2trdx226fR6ysoKEjhzguPuedTJiaTiYbiowTljoy9vwZ69cRnB2UinTt+DoejgYqKPAyGElIp8HrTS8xG2k+XS+2Y3dur9oBKJmHr5V5k/3fF4P2ZKeeR1oDc6y0e9+wnUHtAeb0Xhg0+gRo0bg82p91fXv582n0jBQgzyRTEGtj2a7iy3d7eXkKhXsJhG62t+QSD8VmdBTVSsK+z88m0AJQs/wjtXb/tPgMV/+lC2b8Lae28Cdk/s3lwD6hIJHL5fjUAJUrwhLlg0uOssixz/PhxKioqhl1nzZo1HD16lI6ODlKpFK+++ipFRUWUl5dn/TyhUIimpiaamoZPeRUEQRAEYSaqx+u9gN2uZmRcaQaUJEmYzWYkSW1EfrUZUPKvnyX1pbWkvrQWOtugs63vtvzr9Ea44fBuCk0tw26vzg01rUZCoQQFBeXceeddfP3rX+LTn76Zqionev3o4923+bZl3rgwLEVR6OzsBMBqzQPAYlHGJfikOZLak/a6OTKcx0OnIu45uIeamj3Mn7+bVErCalXG3FQ6UxPymX4etbS09DUgN5nGP8BiNpuprd076nol0itZbW8s59JwWT1DZZqod+7cOeJxMJuLsVgUurpmb4rNaMcpEtkzaIJgV9cbSHn9779Jy3GiGz8/oftosagBqHhcDUANzYASAShhLpj0JuQ/+clP6OnpYe3atcOuU1lZSX5+Po8++ig6nQ673c5f/uVf9k0OyMbvfvc7nn9e/YvDz3/+8750Y2FuMZlM4rUXJow4v4SJJM6vzCKRCFVVbyJJOvLyionHdbhcCk7nlV105uTk0NMjo9frsNkcV7wdAD7zp3SlZKTf/nzQ3eb7/wjrg4+krd7UlN66YKg7Vp3G43mS2to8LlcM9okksushZbQaM17winMsM7/fj06nIy8vD5stH4MB8vNlLOMUf4okIgSkKN+szG79gZlIZx3buSPvInARo3E/FRW3MNaX0GazYTabiUbjahmqMXZV59FwJvP8CgQC6PVmysqqyM21X93P8XDP0fNNHjj6Rf7vCtIm2tUH1OMEST61amzHaTTZvE9Af8DwUjyP/Nx8AE6dOoUsm1m+/FoKC63MmydPyLGZCkPPr2yOUyDwXQoL70RRFA4f/lbaz3Qw/kOKPvPb8d7VPh6PG6PRiCTpcDqdSJKEyWQiJycXm81GTo485p9nYWKI348TZ1IDUE8//TQHDx7kb//2b7HZbMOu9y//8i84HA5+8IMf4HA4OHjwIN/+9rf5+7//+6yzoNasWcMtt/TX8IZCmdPbhdnN6XSK116YMOL8EiaSOL8yu3BhBx7PBQA6O18lHF5FLJYiFLrySVfxeIxgMEB3t5lQKD0NaixTwXZFFYZ2EIks/gDJDK+lojzJL3+5jXA4h/vv/1OsVh1HThzm3T1nKCioYdmyWm67Te0LEo+HiGcYyLbKbaHOnV4WNLCHVCKSIEF6TxxxjmXW2NhIPB7HanUQCITR6RTi8SSJcWwrNFzvr0wGllfddI2v7/5U6lsYDL/gSl5Ck8lEd3eIjo4WDAZXVr3IhjuPhjNZ51ckEqGtrY1EwojJZCce77mq94PhrPnoGla9MP7HaSTh8G56et7Oen11ot5DhEIh2tvbOXv2HPG4lUWLKliyJIDZzBWdL9PR0POrpORnaevs2WPkxRd3kEic4LOfvYOammWEQiGOH38Wi+Vk2vo9PW/T2vpyxumT4yGZjJNIJAiFQoRCITo7Oy9nQ+kJh8NEIklCodkRIJzpxO/HsRlLsG5S8jBlWeZ73/sehw8f5u/+7u/wer0jrn/27FnuuOMOXC4Xer2e66+/nsLCQg4fPpz1czqdTkpKSigpKbna3RcEQRAEYZoIh7f0fR2N/jNw5SV4oJbWqAGeKPKQa9ZALMCmfZt4+finefXEZ1n+zHI27dtEIBbIsF+7ef/iC+gPvUOPZOSN6rvYlrOEHslE2+9eT9s2qL01IxGJqqrFeDwSRqNCdeUK7rjjASoq6pg/XzdqU9rHFnuGXfZIJfz5klWDyk6E0XV0dACQk6OV3zHuo+sfWzC2v6w/UqkGOJbn9p9IOt1uotEre21dLhc6HfT0BEmlpBH355FKWF+7/oqeZzK0tKhlVLm5heh0ugnpAaWZ7OOUTQPzgerc8MVadaLe4cOHicehomIJDochLYNyLli0KMmyZcXo9dDU1EQkGSEQCBAM/vuwjxnrMR+L/hK8wU3IjUYrIErwhLkhq49ssiyTTCZJJpNAf92qmkIo9d1WFIVUKkU8Hkev16PX60mlUvz7v/87TU1NfPOb3+yrOx/JwoUL2blzJwsWLMBms1FfX8/Fixepqqq60u9TEARBEIQZLhzejST1/zFKUfag0+1Cr7/xirc5sAfUwCBRIBbIairY0NH136wwc23VR/ift36a4LE3+O4+N7c2nCR6LsX8+f1XF4lEAp/PRzQqcc01S/F4ZPx+NdqUny+Tny9TUTFyFkc24931xhMZm+8Kw2tvbwfA6SwA1P5P42m01w3g8Xo142mggQ3CNVf62ubk5KDTQW9viFRq16jn0erC68f8HJNFC0C53UUAjKFjx5hk8/M23sdpaAPzCxceGLUfVKx7KwnbRzhx4gSxmER19TJycsY/I2wmcLkUHPkO3nW9yS8anyL8415ucln4x7rhm8lrvaIm4j3TYjECkEgMbUJuB0CnE9lPwuyXVQDqrbfeYsuW/r84fvaznwXgP/7jPygoKOi7DfDUU0/x1FNP8cADD/CpT30Kn8/H7t27MRqNbNiwoW+9RYsWsXGjOqFh69atdHR09N3+yle+wk9+8hO+9rWvEY/H8Xq9fOELX2Dx4sVX/x0LgiAIgjAjZfrLtMHwTxiN6VOmsmWxWJAktQyvpaWZ06ffo6PDyi97/huffuSpYI+ueDQtSFXljfFm8CUuHjrH82u20dHh57dnTZT/7mUeeOAuiorUC4yGhga6u+Pk5pZSWOjFZksSCPRffBgMyqjZT9n8pT6V8BFJMGEXVLOR1oDcblf76Ix3ACqb10071zRDG4RrrvRi2el0Xg5ABZGkfx51/aD/3yhwfWxMzzFZWltbAXC7iwEmLAMqm9ct1r0VJvA4DQ1I7d+/n5d37sRuruX22x+ktjaByQRHjx4lGo3hcJTg8RTico1j/egMEowH+Hbrlzjr6S9dfahy+OCTZqKC9gObkMuyIjKghDkpqwDU6tWrWb169bDLf/7znw+7bPHixSMuB/jyl7886LbH4+Hxxx/PZtcEQRAEQZgDhptwpNPtJpHYBVzZxYKWARWLRXjzzd8RjXbS1KTnwMrfssKTeSqYNupcUZRhR9ef6fbxw5M/5M8e/DN+9KNnOX/ex+uvF/KJT1yP261cvkCUWLFiGR6Pmp0wsJQwmywO7WLU/8IP+evw23x0/k5kSeZ/H/XwkGEFn7+1h0TsXWDiLqhmm2QySVdX1+UJiV4SCbBaxzegob1usizzD//wPbq7Y3xi7SN4cgp4v+UEGxvW0WXsHPQYbcJZJlfy2rpcLiRJDUDF4y/w12fupL7jIDs+1D+NrycJ9+xSv16ZL7FjGhYiKIpCS0sLigJut9p2Y6IyoIYGfwCiyei4NhwfqyVLlvD222/T1XWOUChIe7uT0tIUR44cIZGA2to6TCYFq3XKdnFKba7fzNke36D7BgZ2N6zYwMYbNk7a/uj1egwGPclking8OSAAJabgCXPH7J3FKQiCIAjCrDFS9kEweOU9O7QA1LFjPs6eDRAOu6j7wE0kjJGMF/0DR51v840+ut7j8XDffR/H4VA4dOgP7Nt3ntbWEO+/f4Fk0khlZS1ud6YAVHZBj0AswAPJZ7lgeJXluTJ1bqh0+NlneaMv+ATpI8iFzLq6upBlGZfLTSqllsuMdwaURqfTUVlZRm+vxNu/7+Tdd42cP9XFff713OW6C69F7Zn64fycjNlPmit5bbWWGOGwWud3qKOe+0v7g0+gfn1/qfr1wfaDY9r+ZAmFQvT29mI0WnA43BiNyrj36xrJVAafAKxWKzU1NdhsMqdPH8Lv19HU1EFTUxOybKaiYuGcLb8D2HZq28jLfSMvnwhGoxohjcUSfSV4JpNWgjfpuyMIk06c5oIgCIIgTGvDZT9potErD66YTCYkCVpb21EUqKj4AF965MbLU8HS19cCTB6zB3/MP2qQKpqMUl1dzR133ITFIvOHP/yGHTv20dMDZWULyMszY1TjHOj1/YGObMuINtdvxtflG7Qfj1RmzpiZyOa6s4XWgNzl8iLLamP4icxKWLCgglhM4syZg3R3SzQ2NmLBzP9c+T85/LnDnPnjMzxx3ZJRtzPW11YLQEWj3XQnu1BQRjyXFRSC0WD6ClNMK7/zeouQJGlCG5BPV8uXL8dggKamwySTKXbtOgJASclijEYTLtfcOyYAkWQEf9Q/4jrae/RkMpnUN/xIJEY4HEaWwWy2otNNbvBUEKbKVcyNEQRBEARBmHjZXFxfaYmZlgFlNiukUlY++tHFmM3atKvMI5gfqYSj0Qfw+Z8bdSS7liHxwQ9+kNbWNvbvP8upU0cAifnzl5A7YKrZWEvwQP0Lf6YsrEwikT309OzG4RCleMPpb0A+Mf2fhlq0aBFu936am5sJBPYSjTbh8egoLy9Xn99gyVj6dbW0kdnhcAiHzpWW/aTRsqC2X5JwWVzjvh9Xq7m5GQCPpxCYuPK76ay0tBSv10tLSyfnz5/k0KGT2GxQVVWHwaBgt8/NAJTVYMVj8YwYhPJavJOexWa+PI6wtzdMKpVCrzdgNJpEA3JhzhAZUIIgCIIgTGvl5c9TU3OJgoJjbN/+dZ577v8QjbYRjbYhSa3U1Fy64ot0LQBltyt88IPLqKgwZjXtyrCnnc/lD7/doSPZJUniE59YQ1VVLno9yHIOkch8enr6/+Q9MAMqmxI87S/8I/UHGqqp6UlSqezXn2uGNiAf7/5PQ5lMJj784VspLU0Rje7Eak1RWlrSd5E6UQwGA3a7HUmSCYdDfKFq+EuCRyrh2vxrJ3R/rpSWAeXxTGwD8ulMkiSWLVuG0QiHD79KPB7FYCi+3Hx87h2PgdbXrB95ee3IyyeC8XLKa1eXmlFosYj+T8LcIgJQgiAIgiDMCD09Pcgy2GzOvvuu9q/GFov61+9IxIDReAPNzbqsMq5uv2U3NfmZM6RADVJ9sXbwSHaz2cy6dfeyaFER1dWryMuDrq7+j2IDM6CyuZC2GqzDlgoObw9nzuwjmVRvhcO72f3OH3jtNTNNTeJjoVaCN1ET8DKpqFhIQUE5iqJcvl054c8JaiNynQ50ui3Y9MP3CXIY4F9vuGVS9mksFEWhtbUVr/cCbvd5YG5mQIE69Emv12M0quVk8+erYznncv8ngA11G6jNrc24rDa3lkdXPDrJewQFBe9TXHyeQEAEoIS5SXzSEARBEARhRlADUBJWq2PctllYWEh5eTlLl95Cbq6dcFjqy7ga+G/eNWeoqbmEw9FMPN5KYeE1o2471r017T6Px8PnP/9p1q5dSG6u3DcBD9QLkI4OHQ0NBjo7s/uIppYKjo0s/zP1J3v4X7//W14+/mkusp4N7y/kifc2EYgFxry92SIWi9Hd3Y1Op8Nk8gCTE4CSJInrr7+NcFiH36/Daq2Y8OcEtQxPksDlSj9Ph1LC/znxOzRGXV1dxGIxFi16B51uMzA3M6BAbUa+cOFCjEaw243Y7Ys5c0ZPd/fcbirkNrvZfs92NqzY0NfU32vxsmHFBrbfsx23eUzR+3FRXf0SK1fuTsuAEiV4wlwhekAJgiAIgjAjhEIhUqnBGVBX27TVaDTyqU99iqYmHX5/alBAaCCtT0hJiUxJiQz0l/x1d0u8+66JjkCcW26SKC0dPeugfzv9JAlMJpn8fIWuLt2o2xmtVHCgx+vhUBBq3LX875p/5a+P3IvbdJJ1aqIE83M6+EXzf3B4x6tTdmE21bTyu9xcL6mUHp1OYYIr4fp4PIXcdNOdBIPdWK2lwMTXSebk5KDTwYEDP+W2226kpEQmHJZoaDDQkwzwwWvt07opcktLC17vBXJzG4FGkryO0bhqqndrylx33XX4fD5WraojGLTgcslZvY/Mdm6zm403bGTjDRuJJqNTOrkwHN6Ny3UKlwva2w8AFkwmKyAyoIS5Q2RACYIgCIIwI/T29iLLYLXax33bJSUyS5cm04JC2ZBlyM+XWbTAcNUXe9dck0rLjBrOWCafaX2iTgV8/P2JR2iMnEybnAfg6/Kx5dCWrLc7m2gBqJwcrfzu6gOcY3HbbYu59dab8XonJxNCK8Hr7Q0iy4O/UYdh+gcgW1tbqa7Z1Xe7IbqeG59fzqZ9czOTLz8/n69+9avcdtst3HhjnIKC7N5H5pKpDD7B4PfskpIXAVGCJ8w9IgAlCIIgCMKMMDADqr1dLVULBKb+o4wk0bc/V9tHaSyBsKGlggsWXCTQ80vWvGHl8frB62qT+QBOdB/PODlPW77Nt+2qvoeZSpuA53DkAZNVfgc+n4E33jDT2qq74iDolcjJyUGSFHp7u/t6gs0kbcFXKMjrzwCsc0OZuZMth7ewbse6ORmEki5HTK8moC5MjHB4N5HInr7beXmNeL0XMJlECZ4wt0z9pzZBEARBEIQsaD2gbDYHJpOC0ykPmhw3VXJy1H0oLk7i90/dRytJkli2fBlRXSTjZLy++yRGXN4Z7SSajE7IPk5nWgNyh2PyGpCbzdDUpMdolGlsnNzOGFoJ3kwMQLW0tJBTtD3tfpHJJ0xXmTJWa2r29JXg6cRVuTBHiFNdEARBEIQZoaenpy8DqrIySXFxivz8qf8LvyTBokUJnE5lykteRpqMp2U51Q3Jfhq63GvxTnmpylTQSvCs1oLL/09OcPPDH45SUiJTUTG5USA1A2pwAEqZ+nhuVk6d+jnzPT1p94tMPmE6Gpr9pMnLu4jdfhIQJXjC3CGakAuCIAiCMO0pikIo1IOigM3moLxcprx86oNPmkxNxaeKOhkvlHHZI5XgMDqA9It3bXkD6ydq18ZFJBnBarCO6zbD4TDhcBiTyYRenwNMTgYUwLXXJrn22slPQTIajTgcNvz+KKFQD6A1Q/4+UTmM0vEgETmKrXBypvJlKxKJYDD+dNjlj1TC1w71Z/LNxWCqML2M1K/Pbn+aZHK9CEAJc4YIQAmCIAiCMO1FIhESCRmTyYbVagBmWM3QJBltMp6a+ZQ5+KQtX114/fjv2FUKxAJsrt/Mz079jM5oJ16Ll4dqHmJD3YZxmdinld+5XHmAhNGozIkLQrc7h/Pno4RCIRq72/iz1zawaeFBJD2Uv/CP6jpmNw/XPjxux/pq+Xw/I897cdjlWhbUxdjczOQTppfhsp80BsMBZHkXOt2Nk7hXgjB1RAmeIAiCIAjTXigUQpbV7CeDYYbUCU2BsUzGG06se+s47Mn4CcQCrNuxji2Ht9AZVcvkOqNjazYdDu+mK/TGsMu1AJTTOXkNyKeDnBwnkgTnuk6y+herWWA5iMMADgPcX6quE4gFpk1jb0VRSCR+MOp6j1TC+trpncknzA1ZvSfr/3FOBLwFAUQGlCAIgiAIM4DW/8nhcGIyTfXeTF/l5c+n3TdcGZLT6eTSpUs89YOnSMVyuPvur1JWlpryPlZDba7fjK/Ll3GZ1mx64w0bMy7XMqcW67eSkJN8y5c5c2poA/LJ6v801VwuF5IEPwh8k7gcH9Sc/pFK+O9L/bdHO9aT4cKFC/z+9+sIh13cdtdD/M3ZezkdTD83anNr2X7Po1Owh4IwWHn582zat4kth4dviv9g8Y38w/SqdBWECSMyoARBEARBmPa0CXhWq8iAGquRypCcTic2k41EIkI02kssNok7lqVtp0ZuJj1cs2ktc2pX4xaW5CSpc0OZOXPmlBaAikQKaGgwEAhI4/cNTGPaJLwLqdPcX6pmPmkGZkFpprqxd319Pb29EvPnr6DM6+aFe7ezYcUGvBYvoDbQ37BiA9vv2T4tygUFAUZ/D3ul/Tl0OvF7TZgbRABKEARBEIRpb+AEPJEBNX4kScLr9WIwKAQCHcRi0yvwEklG8Ef9I66jNZseSsucGprVA/3ZPACyLNPe3o6iQCRShKJAODy9jsNEcTqdxPRhkJRBx0kz9L7hjvVkCIVCnD59hmhUT3X1cgoLZdxmNxtv2Mjhzx3mzB+f4fDnDrPxho0i+CRMG9m8hwWTHSSUqfm5EoTJJgJQgiAIgiBMe2oGFCIDagLk5eWh10Mw2DntAlBWgxWPxTPiOl5L5mbT205tY4VLa7yu0hpUQ382T2dnJ8lkEpvNTV6eFZNJoahoepUhThSXy4UNa1r2k2ZoFtRwx3oyHDlyhJ4ehbKyBeTn27DZBr8PiIbjwnSUzXuYy5CH3SzOX2FuEAEoQRAEQRCmPTUDShIZUBPA4/FgMEAg0EE8LqFMs/je+pqRm0lnajatZR2MlNWjZfO0tLQA4HIVkZ8vc+21CUpK5kYASivB+5PK4df5k6r+r6eqsXcqleLQocOEwxI1NXUUFs6N10eYHUZ7D/tY/qfRiatyYY4Qp7ogCIIgCNOemII3cfLy8pAk6OnpQFGYdn2gNtRtoDa3NuOy2txaHl2R3my67VIbH8gxDcp+0mhZUFo2jxaAcjiKL/8/d4IbESLYr30e6whjiax6NQvKY/bwmYWfmbydG+Ds2bO0t4dxOr1UVZXjcIj3AGHmGOk9rMK6kIfKHpvkPRKEqSMCUIIgCIIgTHs9Pb19ASijcar3ZnbxetUGzj097SiKMu3K8NxmN9vv2c6jyzfgMuSp9xmHbzbd0eHnmWde5Itl5mG3+UhlfzZPa2srigJ2exHAnAluaE3a76xtHHXdP6kCf8zPH7/yx4Oat0+WgwfrRfaTMGNp72EDG+ZbUlY+Yryff1r8a9GzTJhTRvh7hyAIgiAIwtRLJBKEw1F0OiN2uxVJSk31Ls0qDocDs9lMd3eUaLSXeNwKTK8gjNvs5q+u28g91m8Sl6PYTGYWL06mrdfREWXr1l9jt5+mJj807Pbq3LC68HqSySTt7e0kkxJudxEWi4Jhjnw61pq0+0L9fbJ6kv29oAZ+7bt8KLXm7Rtv2Dhp++n3+/H5LiBJJhYvXkxOzvQ6NwUhG1rD/I03bGTL1i0E/BEWLrwLp8EtJuAJc4rIgBIEQRAEYVrTJuBZrQ7M5umVnTMb9E/CY0on4YXDuwmHdw+7XOtNZdJZSCYlUqnByy5cUHj66RcJBLq48cb9oz5frHsr7e3tyLKM1erFZDLPqfK7TE3aBzYiH/h1pubtk6W+/hC9vRJVVYsoKxPpj8LMZzPZMJvBYrHh8xl46SULBw/Okci3MOeJAJQgCIIgCNOaNgFP9H+aOGoASiEY7CQ6RdPAOzufpLPzyWGXD22OrgXKIhE4dUrPSy/9ntbW8+Tn21iy5EVqai4Rjf6OHTu+ztGjP6I7fB6jsYWamkvU1FyivPz5vv5PTqfW/2lunF8jNWkfztDm7ZMhkUiwf/8xZBmWLq3D5Zobr48wu9ntdgDmz3fQ1KTHapVpbBQBKGFuEGe6IAiCIAjT2sAJeEajuACdCF6vF70ezp/vxGQyYrMpkzoJLhzeTSSyp+9rm+3mtHXkIbsTjUJvr46WFj0nThzg7Nl68vN1fOYz9+By5QDgdDovrxvCpLMQjw8+f1pbW5FlNQAlSXMnAGU1WFmV76TOPXyZ4lBaFtTFmNq8faIkk0lkWcFgMHLypI+urgR5eSUsXJiHJM2N10eY3T72sY/R0dHB/PkeOjpitLToqahILykWhNlIBKAEQRAEQZjWtAwoq1U0IJ8oRUVF6PVw+vRxXK5l+HwllJTEJ+35B2Y+dXY+mTEANTQDqqlJjyxLNDWd5fjxnXi9Mnfd9XFKSkr61snJUQNR4XA3AInE4G20tLQQj4PHU4TVKs+pUeiPLXAC2QegQM2CamDkkfJXI5FI8NOf/pQjR3rweq9Dls+SSsHixXXk5orgkzA7uFwuXC61pvVDH0oAiZEfIAiziAhACYIgCIIwrWk9oNQJeOIidCKUlJSwbNkyOjqOcuDA8+Tmfoq2tjwKCiY+C2rv3u/h8ezpux2J7BkmC6q/N1V7u45gUIfB0MqpU7/C6ZS56aabWLRo0aBHaBlQPT3dSJKMLOtIJsFggHg8jt/vJ5nU4/EUzJnsJ1CzzAqMTWN+nNa8faLs2bOHixf9BIN6AoF3iEYlcnLszJtXiyTavwmCIMx4c+jvPIIgCIIgzEShUEhkQE0wSZK4447f/wHpAAAgAElEQVQ7+NCHaigr66W+/mecPOmno2NiPypGo1EU5cdp9zc1PZlWcqfdNhoVenok9PoeDh58HkmKsWDBAm6+OT1rymQyYbFYSKVSyHIYgERCjWS0tbWhKAp2ewF6vQGnc+4EoEbqtTWaWPfWcdyTfm1tbbz77gF6enR88IN3UllZiSQpLFhwPeJv5oIgCLODeDcXBEEQBGFa6+3tFT2gJoFOp2PNmjUkEgmOHTvHa6/9HL3+YXS6HDweNfrT2/wiMTmOp/ST4/Kc5879Eq/3Ytr9sryHhoa9VFbehMmk3tc3Bc+ksHJlhJ//fDuKEqCwsJA1a9YgDZMik5OTQzQaJRYLYrE4iMXAaoXm5mZSKcjNLUanU7DZ5s65VV7+PIFYgHU71mGRffxrnXr/4/VwKAiuuJe/8DzOFz79BX76s10cOvAOd955PbffvmpC9keWZV599VV6ehSqq1dy661Lqa5eyJkzMXp6bOTlzZ3phIIgCLOZyIASBEEQBGFa0zKg1BK8qd6b2U2v13P33XdTU1OG0djDa6/9HJ8vzPutQTbt28QrF7/Ca5ceY/kzy9m0bxOBWGDYbUWjEB+ljVQ0+v1hl3V0/BUHDnTT06MGlvp7QCkcP/4KcJ6iIhv33nsvxhFODK0PVCwWBPozoFpbW4nHwestwm5X5lyJ1+b6zfi6fIMm4WlfB02dvJV6CwCHRTt+Ezf57tChQ1y61ALkUFe3ipKSFADz55tZsSI1qQ3xBUEQhIkjAlCCIAiCIExbiqLQ3d0LgMNhn1NNoqeK0Wjk3nvvZf78IiQpwG9ff5pPv3Ifuxq3sMSVos4NZeZOthzewrod6zIGoWQZdu0y85vfWGlqyvyiBS/8Crv99LD7kZfXwDvv/B927NhPa6vSV4J37Nh+jh49isFg4L777uvr8zSc/kl4aiNyLSimNiCX8HgK51T/J822I0+zwqX2ddJok+4Adkf2AtDba8Pv19HcPDFN6UOhEG+//TY9PRLXX38H+fnGOZWNJgiCMJeIj3GCIAiCIExb4XCYZFLGZLJgtYr0p8liNpv55Cc/SUWFl/3W33E+eipjpoyvy8eWQ1vSHq8oEAzqUBS1YXgm7e3/POp+rFz5Bw4efIsf/ehZjh5t5+LFM+zf/yYAH//4xykqKhp1G0MDUImERCQSIRgMIstG3O58HI65lWETSUbwK72DXlONdl8vIaLJKNGoFUUBv39iAlCvv/46vb1x8vJqmDdvAUVFqQl5HkEQBGHqiR5QgiAIgiBMW/0T8ET/p8lmtVp54IEH+PYzfzdspsyhIGzzbWPjDRvTHu9yyZcn1fXwyitv0dWVj9W6gOXLc6islDHsuJG2tkWcXfUVPv7xlbjd6a+v0XiOsrKdXLrUxq9//V/odAaMRoWbb76Z2trarL4Pbdx5fwaURGtrK8kkuFyFGI0SVusVHKAZzGqwsirfSZ07lLZMe23PhnKwGCzk55uRJDAaI+O+Hw0NDTQ0NBCNWrj11jvwelOYzeP+NIIgCMI0IQJQgiAIgiBMW2r/JwmnU/R/mgo6s45ufWzYTJmvHYLOaCfRZBSLwTJoeX6+TH6+THPzQd577wgNDQYU5S127vTywQ9W8NHm8zgUMC2szhh8AqiqquLP/uzzvPHGbt58s55gMEl5+VLmzbsZyC4gqWVAhcP9JXh+v1p+V1paPCfL7wAeW+AE0gNQAH9ZCy+c/xgApaUmPB4Zk+nKA1Dh8G5iqRi5zo/03ReLxdi5cyfRKCxevIqcHAeFhckrfg5BEARh+hMBKEEQBEEQpi2RATW1ssmUuRjzpgWfQC29CwZ1XLyoZhuVlJRwQ+te1oR3ws7+9XI3f4YUIN2zHt3ah9O2YzKZuPPO1SxdupBXXulgyZJldHXpKS3NLlihNSHv6elGp1OQZYnmZq0BeSFO59w7r8Lh3RQYm4ZdXmqFhsjz3P3CGR4v/y4A0ejYA1CBWIDN9ZtZrN9KQk7yLZ+Xh2oeYkPdBt7b9R6hUA+W/5+9O4+P667v/f86s4+kkUa7LVu27NiSYye2QpwAIUkDPwihJG5qIJiU9udSuNDY0HJ7aX91fw2P9N70V2gLTYoVrsttGwLBJMEsAZKULJDFjhMc74tseZFtyZtGGm2zzzm/P0ajxdptSTOS3s/HI4/MnHNm5jM+R8t89Pl8vp4KqqtvYM4cE7v9it+SiIhMA5oBJSIiIlmrq6urZwU8JaAyJVUpM7T1VbCuZt2g7YlEagaUaVo0Nl4gkTC4666P8q6v/jPtt/7uoOOHSz71N2/eHD784ZXk5NgpKhr7zKacnBzsdjuRSARIzTE6ezY9gHwOubmza/4TQCDwjVGP+R81sPvSbj67+4N0OduJRqOY5tj/rdoibax9dm1qeH1+YsDw+nu23cObe94kErFz440fxus1xnVORURkelICSkRERLJWugLK61ULXiaMVilT64fP1qwetD0eNygoMIlE2rGsEE5nDrm5+eTkGBR+cIgE1OpbxxRPRYXJddclqKgYe7LCMIzeKqhYrJ1QqIvW1m4cDjclJYWzcuZQZeUzVFc3UV3dxPz5zfzZc/m8/zfQ1a+orKDn6y1uxdg+55dYVqptbqy++dY3qW+rH3J4/YmuE+x0vkVl5U0UFZUzd24Sw7jqtyUiIllOCSgRERHJWqkKKEMVUBkylkqZaMeWQdtisdQMqPz8M3g8UFAwF8Mw8HgsrF2vg68A2+f/Etvn/xJ8BVi/fWMywu+VngMVi3XS2nqecNigqKgc3/DFXTOe+bMnSX5uDe4/u4f93g4+Ng/y+g3nyHPAx+albl9yNwEQDo+9De+Jg08MO7weYJ/9EIHABzh92kZ+vr62RURmA82AEhERkayVbsHzenNVAZUBlZXP9N7+4Q+3sWtXI7e9/8Pc85HlIz4ukUiVswQC50gmwe+vwGazcLtTo8NtD30Lw5fKRNhqrsd65ZeT9h6gbw5UOBykpSWEaUJJydxZ2X6XZltzPybQ9ovvYRkMO2j+R02AAWEr0tPGOLpwIkwgHOD/rR76Ob+8F2LObqx4glBIX9giIrOFKqBEREQka3V0dGFZkJeXpwHFGVZWVgpAR2tw2GMikQi7d+/m5MkEDQ0OTp48j2VBSckc3G4wjFTiI518AjB8BdjWfGpSY08noHbufIU9e/bS2mrDMCpm5QDy/owbb8VvOgZVP6X1VkFZBm7LM+YWPK/Dy+1l+QOqn9LSVVAFjmKuWehk6VKtfCciMlsoASUiIiJZKRaLEQpFsdsd5OV5Mx3OrFdeXg5Aa+vFYY956623ePnll3nxxVdIJpOcP3+RuXNPU1h6FI8nc8meFStWsGDBAhyOBF1dEcJhg5aW+Vy8OLt/FbZ2vY6VV8AXFg9/zPoqKE8s4tQpB2fOxMb83F+sHnl4/adXrOPeeyPccIMSUCIis4Va8ERERCQr9bXf5eFyZToaSVdABQLDJ6BOnTqFZcG5cwdw5ZdwrOpF7rzltzQmn+JPXi7mU8s+yYbaDfjdQ5TGTKL8/Hw+8YlP0N7ezquv1rN3r59ly3JpbU2Oa6D5TLM/1sQPP/Im60fIw+U54EvFK3nNgnPnxlYBFQptp8TeNOz+Wj/cUT54eL2IiMxss/vPPiIiIpK10gPItQJedvD7/TidbsLhbrq7uwftD4fDXLp0iVgM7HkhHrf/Ed4lb7Gy0KTWDwu8Aer21bH22bUEo8O38U2mgoIC7rnnZj7zmWUsWJCkqGgWJ59a9vPRtke4f0Fo1GNvuu6/MAxwOMY2hPxKh9eLiMjMpgooERERyUpdXV0kk2gFvCxhsxkUFpZx8eIZLly4wOLFA/u2zpw5A0BubhmHnT+hzdnCg1V9+9PDp+vb6qnbW8emmzdNXfCXqagwZ3XlE8AfPf9HWFjYjb5tSQs++Grqdrm3nHc+/Q5nz9p5441dFBW9gts9tgRUZeUz+Hw+Ojo6+Pa3f0lbw14Kikv41Bc+R1GRMfoTiIjIjKQKKBEREclKXV1dnD9v49gxP0eO6G9mmWYYUFSUmgN16dKlQfsbGxsBmDt3GSf9B1lVwIAh1Onh0wBb67dOerwysovhi3xpCQMSUHYDvrQkdftC+AKQOu/d3Tm0ttpoahp6BtTBgw5+9Ss3b7/t5NQpOydO2Dl2zOAXvzjIwYPHqOgM8d6CSmw2ffQQEZnN9FNAREREslJXVxfBoA2nM5+mJi2Bl2mpBFRqDtSFCxcG7T9z5gyJBPgK5xCik/VVg58jvS0QCRBJRCYvWBnRua5zAKypGLyv/7aL3RexLIhGc5g//zSm8c6g42MxqK930tFh48wZBx0dNrq6bLzzzl5ee+1FIhGD1QVuCi81YJqqfhIRmc2UgBIREZGs1NnZSX6+idudR1WVVsrKBsXFZQBcvDiwAqqjo4O2tjbAzdzSKt5X7BtQ/ZSWroIq9hTjcXimIGIZyty8uYOqn9L6V0GV5ZbRGg7yRs5jrPjIUxTX/isrn1jJwzsf7p3jZZpQUGDidFpce22MhQsTdHXt4vDhn+P3J7n77ltZGD1NWds+EvoyFhGZ1VTPLiIiIlmpu7ubkhKLO+5wU1urT67ZID+/GLvdQVtbkEgkgseTSiKdPn0aAL8/1Wa1cakP6BzyOdZXQQPrpihiGc5Q1U/99/2wqZxgNMgDb68l31vPqsLUvvnu1DD5l868xLZ7tuG2/JSWmlRWWlRXJ9m9ezc7drzM+1rPcNPFE3DiN73P6/mbu0kCxj3rsK25f3LfoIiIZB1VQImIiEhW6uzsxLLA681Do2Oyg8Nhp6CgBICWlpbe7WfOnMGyoKhoITbbG8xxNw/7HLV++GzN6kmPVYZ3/vymIauf0uwGfO/Wm9i8ZzMnu+sHtFOmb6eHyUPqiQwDdu3axcsvvwxA4R/9KcY9gxONSj6JiMxe+nVOREREso5pmoRCISwLcnLyMAytgpcNUnOgyrCsvjlQlmXR2NhILAZlZQtxuf5x1OeJdmyZ7FBlBB0d3xv1GEfsObYe3TrqMHmzZzHBAwfe4te//jUAH/zgB1m9ejXGjbcOel5j9eBtIiIyO6gFT0RERLJOKBTCNE1crlzsdgd2ezzTIQmpBFRhYTltbX0r4bW1tdHd3Y3NlovfX4rP9yPmzjUHPC6SiGjmUxaprj7Njxt+zMZXNvK9m2GeN7X9UhRK3anbx6zP0xqp429rBj9+fRV8eW/fMPkDBw5w8OBvKC42+NCHPsT1118PgLXrdfAVYLv/8wCYT/5vrN++gbFmwRS8SxERyTZKQImIiEjW6erqAsDr9QGoBS9LBAI2Ojrm0tlp9FZApec/FRUtxDAM8vMHV6sp+ZR9vvjKFwGo6HdqSlx9t7vb67it1Eetf/Asr3QV1NloMXt+u5s9e3bgdhvceeeHuO666wYca3voWxi+VMmUreZ6rFd+OfFvRkREpgUloERERCTr9CWg8gAloLJFOGyQl1dGa6ud1tZWEokEjY2NJJNQXLwQm80iJ0ftktOBhcX3bk5VtaX1v13rh6o81+AH9lhfBbs7P8qOHW8QCjlYuvSjFBVVA33Vb5fPejJ8BRhrPjVB70BERKYb/TonIiIiWaerq6tnAHmqAsoYYWCyTJ3q6jhut5P8/CIiEZNLly5x9uxZolGDOXMWkJdn6VxNA3sv7gUGVj8Nxe8IDLuv1g8fcBQRixnMnXszS5cup7VVHy1ERGR4+ikhIiIiWSe9Al5OTh42mypqssXChSbveU+MBQtK6ey0cfDgQSKRCC5XAXl5/iHb7yT7rCpbNaj66Ur43NuIRg2WLbuW/HyToiJz9AeJiMispQSUiIiIZJ3u7u6eBJRP7XdZpqzMpLS0jEQCfvvbQxQVnaFyUTuGYZCXpwTEdDFa9VOa1/teqqubqH/ri/zq579Ld/fzVFc3Ybc/yu59N+LzFbNyZTHveleCigqdfxERGZ5mQImIiEjWSVdAeb15SkBlGZsNampKeH1XhJdsO/n88l2Yhsmn3vlP7g9/kg21G/C7/ZkOU0Zw/Ph7xlz9FA7vYMuuByhe+iyl1Safeud1ljRewx9X7KW41uSbe18m3HSWryx4QOddRERGpF/pREREJKscOODgwIEwnZ1GTwtepiOSy5UtcPFi5Q9wVr3NykKTWj8szAlQt6+Otc+uJRgNZjpEGUEyeWZcx883fsr1Ped5sa8DM7ab6/2p+0sK2vnPY5t13kVEZFT6lU5ERESyRne3wdGjTi5e7CYUMlQBlaX+T/3/IegKsL6qb1v6dn1bPXV76zIRloxRdXUTH3rVwft/A1a/sV2WBe//Teq/D73q4Om2B3j/b6Ar0XfM+ip03kVE5IroVzoRERHJGqmqpzDxeAyv147b7cUwNNg622w9upVVBamV0NJq/bCqoGd//dbMBCZjljATfOfGgYPIDQO+c2PPfivB1v2PD3medd5FRORKKAElIiIiWaOry0ZubgdFRSZ+fx6GYagCKsuEE2FaI60DqmDS0tsCkQCRRGQqw5JxOBI4AgYszh28r/+2Vqt7yPN8OZ13EREZC/1KJyIiIlkhkYBQyCAU6gTAbvcBKAGVZbwOL7eV+gZUwaSlq2GKPcV4HGNcZk2m3LLiZYOqn9L6V0ENd54vp/MuIiJjoVXwREREJOOslgsE2qLANUQiXQC43UpAZauNS31A55D71ldBA+umNB4Zv6Gqn/rvMzBGPM+X03kXEZHRjCkB9cYbb/DCCy/Q2NhINBpl69a+/u7W1la+853vcOrUKVpaWti4cSO333577/729naeeOIJDh06RGdnJ36/nw984APce++9GMOs//r3f//3HD58uPe+ZVnEYjH+4i/+gne/+91X+l5FREQkywSjQTbv2cwPDz5BINlJgaOE93huw2ebR05OHqAEVLYJhbZT5mwedn+tH+4oXz2FEcl4nTz5gSGrn9IMA753szXieb6czruIiIxmTAmo3Nxc7rzzTmKxGFu2bBmwzzAMVq5cyZo1a3jkkUcGPTYSiTB//nzuu+8+SktLOXPmDF/72tdwOp3cfffdQ77epk2bBtx/8cUXefLJJ7nhhhvG+r5EREQkywWjQdY+u5b6tvrebe2JFl7o+jH+ymJqvbcBaAh5lgkEvjHqMdGOLVBw5xREI1ciHq8f9Zi5V9BJp/MuIiIjGVMCqra2FoCDBw8O2ldYWMhdd90FgG2IP1GWl5dz77339t5fsGABt9xyCwcPHhw2AXW5F198kd/5nd/B5XKN6XgRERHJfpv3bB6QfOov6ArwmvlTbuT9qoDKMpWVzwzaFklENPtnGqlc3MDHti3hX2qH3v/ne2BvOxz/4+N4HB4e3vkwdfvq+OaqvhXw9gThy3tTtzes2sCmmzcN/WQiIiI9pvxXOtM0OXToEAsXLhzT8cePH+fEiRN86EMfGtfrdHZ20tzcTHPz2EuHRUREZOps3f/4iPu3h58D1II3HSj5NL14HV42Lhl+f3pf+rxuPbqVVQUMGEieHjwOsLV+KyIiIqOZ8iHk3/3ud+nq6mLNmjVjOv5Xv/oVK1asoKKiYlyv89xzz/HMM6m/0D311FP4fL5xxyrTn8vl0rmXSaPrSybTTL++wvEwrVb3iMd0mm3Y3Tby8734fGrDm2gz/RqT4T1/6B9Ykjf8/iV5qeTSjos7uG3BbbRGWvnbmsHHra9KVUEFIgGcXueARKSuL5lMur5kMun6mjxTmoB6/PHH2b17Nw8++CA5OTmjHh8KhXjjjTf40z/903G/1kc+8hFuvfXW3vudnWNbwUNmFp/Pp3Mvk0bXl0ym2XB9FXmKaI20Dru/wFFMMmoSCnXR2WlOYWSzw2y4xmRo85I/ITbKMRuXwHvL3ksikuC2Uh+1/sHXSroK6my0mHg4Tpx47z5dXzKZdH3JZNL1NT7jSdZNSVG7aZp8+9vfZt++fTz00EMUFxeP6XGvvfYaHo+Hm2++edyv6fP5qKioGHfllIiIiEyNddUjL9n+gcL7aWhwcPHiCMt1ici42e0Fox7Tlei7vXHp8B8u1lfBupqRv5ZFRERgjAko0zSJxWIkEqmfRLFYjFgshmVZg+4nk0lisRjJZBKAZDLJo48+yokTJ/jqV7+K3+8f9nUu9+KLL/L+978fh2PKOwVFRERkkm2o3UC1vXzIfYtyF/Gx8i/i9yeJxZSAEplI4fDuUY9Z4Ut9TAiFtlPmHH6maq0fPluzesJiExGRmWtMmZ1XX32Vurq63vuf/vSnAfjWt75FWVlZ732Axx57jMcee4yPf/zj3HfffdTX17N9+3acTicbNmzoPe7aa69l06bUahlbtmyhpaWl9z7A0aNHOX36NF/5yleu7h2KiIhIVvK7/Xz/959n/b99gQbffqKOEMWeYtbVrOOBVQ8QCvhobTUpKlL7ncjEiox6hNOe+roLBL4x6rHRji1QcOdVRyUiIjObYaXLmGY4rYY3O6l/VyaTri+ZTLPl+nrzzaNs2/YLFiyYx2e/sEarqU2h2XKNydAaOxq54+k7+MLiGL8/L7Xtx03waAO4bC5+/YlfszB/6FWrI4nIqF+rur5kMun6ksmk62t8xjP2SAsbi4j0Ewx2cOzYeWZHal4k8w4cOAbAihVLlHwSmUIbXt5AzIyxpt/nhvTtmBlj48sbh32svlZFRORKKAElItLDsiweeeQnfP3rT7Njx+jtCSJydcLhBCdOnAJg1aprMhuMyCyz59IevrQE7P1GrNkN+NKS1O3dl0afEyUiIjIeSkCJiPQ4d+4cZ860YZomx46FMh2OyIx36NAZEokYc+eWUVQ0+qpcIjIx2iJtWFgDqp/S0tssLNoj7VMbmIiIzGhKQImI9Kivr8fttjAM8HjCmQ5HZMY7cOA4AMuXq/pJZCoVegoHVT+lpaugDAwKPEoMi4jIxBnTKngiIjOdaZrU19ezZEkjSZLk5ioBJTKZolGT48dTCaja2iUZjkZk9hmq+qn/vtfbb5i6YEREZFZQBZSICHDo5CGeSzxH5S3PsOB9P+LLzb/HX7/09wSjwUyHJjIjHT16jkgkREmJn7Ky4kyHIzKrnD+/acjqpzS7Ad98V9WUxSMiIrODElAiMusFo0E+89pniJf9lpWFJrV+WOoP8t0Tm1n77FoloUQmwf79DQAsX74Ywxjhk7CITLiOju+NeowZ+ekURCIiIrOJElAiMuv96+5/pSnexPqqvm3p2/Vt9dTtrctEWCIzVixmcexYqv1u1Sq134lMterq01RXN1Fd3YTb/d7e7aZ5C5WVzT37TmcwQhERmYmUgBKRWe8HR37AqgKo9fdtq/XDqp7Zq1vrt2YmMJEZ6sSJFrq72/H7c5g/f4RBNCIyacyfPcn5/3U70eiO3m0223Ye/88VtP7kOxmMTEREZioloERkVgsnwrTH2wdUP6WltwUiASKJyFSGJTKj7duXar+79tpr1H4nkiEdH/5d3v7w4Cqnohvb+HjiSbWfi4jIhFMCSkRmNbfNzbsL3AOqn9LSVVDFnmI8Ds/UBycyA8Xj0NCQSkCtXHlNhqMRmb2eOrCJmoL4oO21fvCYaj8XEZGJpwSUiMxqzc3NfG6Bc9j966tgXc26qQtIZIY7fbqdYLAFn89NVdWCTIcjMmuVWL8Ydt/6KrWfi4jIxHNkOgARkUw6ffpnXFPSNez+Wj/cUb56CiMSmdnS7Xc1NVXY7fYMRyMyO7V1vsJ1+Ylh99f6Yb471X6uCmAREZkoqoCSGevllxt45JGdnD2ry1yGZlkWDsf3Rz0u2rFlCqIRmflS7Xep1e+uv16r34lkSldw86jHfG6xQ8knERGZUKqAkhnrl798jc7OEJWVi5k/X79AyWAtLS289tpa7PYc7rhjIx6PwTXXhHn4a98g2p3DAw/8OZWVyUyHKTJjNDeHaGlpJifHzjXXVGU6HJFZq7LyGR7e+TB1+4af87Rh1ef5/SmMSUREZj6VhsiMZbeHMQzwesOZDkWyVHoQcmnpUmw2G4WFJg6HA4/DTTKZIBwevj1BRMZv//7jWJbF0qULcLlcmQ5HZFbbULuBmsKaIffVFNbwwKoHpjgiERGZ6ZSAkhnJsixcrijFxRZ+fyzT4UiWamhowLKguHgpAH6/iWEY5OS4AQiHde2ITJR0+51hwHXXqf1OJNP8bj/b7tnGhlUbKPYUA6lVXzes2sC2e7bhdw+xPKyIiMhVUAuezEiJRALTTN2ORAYvMSzS0dHBxYsXSSZdlJdXkZNj4k7lnfB43ECEUCgCuDMZpsiMcfFijPPnT+N2Q3X1NZkOR0RIJaE23byJTTdv0sBxERGZdKqAkhkpHo9jWanbsZgSUDJYuv2uuHgRdruDwkKrd58qoEQm3oEDpzDNJIsWzcPr9WY6HBG5jJJPIiIy2ZSAkhkpGo0qASUjamhoIJmE0tJqDCPVfpfm8bgxDIjFIiQ0BkrkqsVicOxYA4YB11+v6icRERGR2UgJKJmRVAElIwmHwzQ1NRGN2qmoWEx+vond3rff7XZjs0E0GiGuy0fkqgUCSZqbT+JyWSxdqvlPIiIiIrORElAyIw1MQKmERQY6ceIEpmni9y/A7fYOqH6CdALKIhaLkkwaGYpSZOY4fPgMiUSMysoyCgoKMh2OiIiIiGSAElAyI8Visd4EVDSqOT4yUENDA/E4zJmzFLvdIj/fGrDf4/Fgs0E8HlUFlMhV6t9+p9XvRERERGYvJaBkRopE+pJOCQ3xkX7i8TinTp0iEjGYP38Jfr+FcVmRk9vtJhw2OHUqTnOzfegnEpExaWuDs2cbcLksqquVgBIRERGZrZSAkhkpGo0PeVuksbGReDxBbu4ccnPzKSw0Bx3jdruJRAxisSgtLfo2KXI1jh5tJhoNM2eOn+Li4kyHIyIiIiIZok9WMiP1b7uLq4dK+jl+/DixGMybV4PLZZGTYw06xu12k5trkf2nUYoAACAASURBVExGyM8fnKASkbGJxeDo0VT73YoVSzAuLzcUERERkVnDkekARCZD/xY8rYInaaZpcvz48d72u6GqnyA1A8rns8jNDVFaOjhBJSKjC4W209ZmcPbsMbXfiYiIiIgSUDIz9U86xeOaASUpzc3NhEJh3O5iCgqK8fuHvjbcbjeGkV4Fb4qDFJkhAoFv0NERp7v7Dioqcpk7d26mQxIRERGRDFICSmak/gmoRCKGaYJNDaezXkNDA5EIzJ+/lNxcC7d76OM8Hg+GgRJQIuPU0NDAW2+1U1OTwOfbgdMJc+cuZsWKj6r9TkRERGSWUwJKZqT+M6CSyQTJpBJQs51lWT0JKIPKyqUUFQ3fWud2u7HZIBKJYJr60CwyFvF4nG3bnuPoUZNrrvlV7/Ybb9zOggV/lsHIRERERCQb6CO5zEj9Z0DF46kKKJndWlpaaG1tx2bLo6RkLgUFw18U7p7SqFgsSiKhGVAiY9HQ0EB3d5zFixspLNzdu33OnLOUlJzNYGQiIiIikg2UgJIZqf/Kd8lkQlUs0lv9NH/+Evx+sNuHP9bhcOB02jHNJLGYZoiJjMXhw4eJRg3e9743Bu1ra/uXDEQkIiIiItlECSiZkaLR/kPIVQElDGi/8/tHvyC8Xg8AkUh0skMTmfa6u7tpaGikvPwsc+cOrnYKh3cQCm3PQGQiIiIiki2UgJIZaeAQ8rgSULNce3s7TU0XARfz5lWSnz96W53Xm2rDi0SiWOrCExnRkSNHCIct3vOeXcMeEwh8YwojEhEREZFsowSUzEjRaF/VSjIZ1xyfWe748eNEIgbz5i2muNjOWBbjcrvdGAbE41oJT2Q0hw8fprDwDMXFx4Y9RlVQIiIiIrObElAyI/WvgLIsS3N8Zrn+858KC8dWDpdOQEWjESWgREYQCARobr5Abe2bYzhWVVAiIiIis5Uj0wGITIb0EHKHw0k8Hu9JQOlyn43a2l6ho2MHsIhFixaRkzO2aji3243NBvF4RC2cIiM4dOgQsRgcPvw/mT//wyxenKS52UZrq42iIpOKCn0BiYiIiIg+kcsMlEwmSSQSGIaNnJwcwuFQT0WUJ9OhSQZcuPB1ampa6ey8nfJyFzCeCiiLWCzas4qi2jhFLmdZVu/qd4sXr+idr1ZRocSTiIiIiAykBJTMOLFYatU7p9ON2526xPuviiezRyi0HZttH+XlUFMTGtPqd2npFrxYTDOgRIbT1NRER0cnTmcBZWWV+HxqdxYRERGRoWkGlMw48Xgcy0q137ndTkAJqNmqpeWfem8vWvQMbvfYH+vxeLDZIBbTDCiR4Rw6dIh4HBYuXI7Hw7i+xkRERERkdlECSmac1PwnA4fD1ZuA0hDy2ScU2k4ksrP3vt3+1rhW4Oq/Cl6qBU9E+kskEhw9epRo1GDRohX4fGq5ExEREZHhKQElM04sFuutgPJ4XADY7d+j6eIjGY5MptJQq22NZwUut9tNKGTQ2BilqUnfKkUud+LECaLRKHl5c/D7S3rnP4mIiIiIDEWfqmTG6ZsB5aLTHuT5+d/DlvMoF1q+zvx/m8/dP7mbxo7GTIcpkygU2k44vGPQ9nB4x5iroEpKSojFDNramjh3Tt8qRQDCiXDv7UOHDpFIQGXlCmw2i9xcJaBEREREZHgaQi4zTnoGVLeznUc7HuL3liTI67nS186z+FHTbu54+g5+/YlfszB/YWaDlUkxUqVTIPANcnJuGfU5iouLqajI49ixbkKhZqBkAiMUmT6C0SCb92zmh0d/SCASoNhTzMcWfwzXSRfJuIeqqmX4fBaGOlVFREREZAT6s77MOLFYDIBn3f9OggTrq/r2pW/HzBgbX9445bHJ5Buu+iltrFVQhmGwevUiiotNOjsb6LmsRGaVYDTI2mfXUrevjkAkAEAgEmDLoS38wPsDcorL8HrzNP9JREREREalBJTMOLFYqgKq2TjFx+bRW/0Eqdsfm5e6vfvS7swEKJNqLHOexjoLavHixTidFo7Qz7h06idXG5rItLN5z2bq2+qH3BewB9ifn/o+qvlPIiIiIjIateDJjBOJxIjYwmBYA6qf0tZXwY+awMKiPdJOgadgqkOUSVRZ+Uzv7e9+92kOHDjLmjWf4NZb54/7ufLL8tlT8jo33PAWbwQf53898SCfrP4kG2o34Hf7JzJskay09ejWEffvjP0XOTkWDv02ISIiIiKjUAWUzDjRaAyP6R1U/ZSWroIyMJR8muGi0TgAbvf4Px0Ho0E+8dwnMCt2cn2hRa0f5rsD1O2rY+2zawlGgxMdrkhWCSfCtEZaRzymPRHAlROaoohEREREZDpTAkpmnFgslXT4TNXwl/f6Krih9IYpikgyJR5PAuByOcf92HTr0VAzxOrb6qnbW3f1AYpkMa/DS5GnaMRjChwllBa6pygiEREREZnOlICahoLBINu2/ZTnnw/w5ptOmpt1GvuLRmOsWPFbchzDD8XNc8C/3HzrFEYlmZBORl5JBdTW/Y+zqgBq+3Xa1fphVU/R3Nb6kVuTRGaCddXrRtz/btf9tLVp+TsRERERGZ0yF9NQQ0MDhw83sHPnAdrabLS26jT2F43Gede7Rl/lzAr95+QHIxnVl4AaXwVUOBGm1eoedoYYpFYCiyQiVxegSJbbULuBufa5Q+6ba1/GvSVf1M8gERERERkTjQ2dhpLJJJZlYLN1YJoGRUVa/rq/aDTGE098iY985F5WrMjlm9/8PmVlVbz7d+4kz+Fn3rwkxcX6N5sNYrEEMP4KKK/Dy22lPmr9nYP2paugzkaL8Tg8xIlPSKwi2cjv9vOHiT/k+c5XOFV4nI5kK36nn3U1n+KjBX+GI5Gvn0EiIiIiMiZKQE1DqQQUOJ2dLFyYoKJCv/z317/qxeVyYRiQSMTJc6R6qcJhtYvMFvF46lpwucb/rW7jUh8wOAEFqSqoBkZuTRKZCSKRCInOBKuDH+D/+7+eZeE1IQp9/Wc+JTIWm4iIiIhML0pATUOJRALLglCoC9M0SCTQEtj9RKMxAFwuF06nE8OwSCT6qlQiESWgZoNkMkkyaWIYdlwuO2CN+bGh0HbKnM3D7q/1wx3lqycgSpHsFggEsCzIyyvFZrNRkKuB4yIiIiJyZTS4YRoyzVTFUzQaIplMEo8rodJfugLK40knoBiQgEq2/Zyu5l9kKjyZIvF4HMsycDic2O3je2wg8I1Rj4l2bLnCyESmj0uXLpFIgN9fitttYdNvDSIiIiJyhVQ3Mw2lW/AsyyIS6SYWy8HrzXRU2SOdgHI6nUMmoGy2fyIQsMir+GimQpQpkEgkWFzSAA4HdvvYq58AKiufGXDfNE3++m//BStu8OCDD5CXpyoQmR0uXbpEPG7g95eQkzO+ryMRERERkf70t8xpKJ2AglQbniqgBorFUi14Hs/AGVAAdvvrWL7DRN1HCIVGXylPpqdgNMg/7PoH/Lf8FN+7n+Hmp1fy8M6HCUaDV/R8NpuNsqJSAM6da5vIUEWyWktLS28FlNerBJSIiIiIXDkloKah/gmocLiLaDSz8WQTy7L6DSEf2IJnWRZO5z/2HjuWNiuZfoLRIGufXcs7F/6D6wstav0w3x2gbl8da59de8VJqJKSIgDOn2+dyHBFspZlWbS0tBCPGxQWKgElIiIiIldHCahpKJlM9t4Oh1UB1V88Hsc0Lex2Jw6HDbvdjsNh4wOh41zz09uAHb3HhsM76Pq792P+7MnMBSwTbvOezdS31bO+qm9b+nZ9Wz11e+uu6HnnzCkG4MIFVUDJ7NDR0UE0GsPpzMXjycXjUQJKRERERK6cElDTkFrwhpcaPJ2a/5Qelut0OnnJu4jzHx68XHjrmgS2NfdPcZQymbbuf5xVBamV6tJq/bCqoGd//dYret45cwoBuHgxcLUhikwL6QHkBQUluN3WuIf5i4iIiIj0pwTUNDQwAdVJz8gjITX/ybLA4XBhs6X+kVwuJ3PnnsYsPDHo+EjOCc2CmkHCiTCtVveA6qe09LZAJEAkERn3c8+Zk2rBa21to2chSpEZLTX/Se13IiIiIjIxlICahi6fAZVMGvpA3COdgHI6XQMqoN71ruGTTJoFNXN4HV5uK/UNqH5KS1dBFXuK8Tg8437u4uJCHA6Dzs42uruToz8gi3Sf+wXd536R6TBkmkmtgKcB5CIiIiIyMZSAmoYuT0ABqoLqkW7BS1VApbaVl59m7tyzwz4mHN6hKqgZZONS37D71lfBupp1V/S8DoeDwsICLMvi/PkrG2SeKYHAN5RolXELBAI9FVBlSkCJiIiIyFVTAmoa6j+EPBJJJ6A0BwrSFVDGgBlQixa9NOrj9OF8ZgiFtlPmbB52f60fPluz+oqfv6wsNQdqOq2EFwptJ+I6QsR1RIlWGbNEIkEg0EoyaVBQUKwElIiIiIhcNcdYDnrjjTd44YUXaGxsJBqNsnVr3xDf1tZWvvOd73Dq1ClaWlrYuHEjt99+e+/+9vZ2nnjiCQ4dOkRnZyd+v58PfOAD3HvvvRjG8EmTEydO8P3vf59jx45ht9tZtmwZf/VXf3UVb3XmSFVAGT23o8TjMeJxO6APCAMroFL/HkeP/jl79zYCUFRkEgoZ+Hzz+ZM/+SRFRfo3m2zbt7tobbXxrnfFqKiY3F7RsSQSox1boODOK3r+srIiDh48ycWL0ycB1f/fJBD4Bjk5t2QwGpkuAoEA8bhFXl4RXq8du33wIg4iIiIiIuMxpgRUbm4ud955J7FYjC1btgzYZxgGK1euZM2aNTzyyCODHhuJRJg/fz733XcfpaWlnDlzhq997Ws4nU7uvvvuIV+vqamJhx56iD/4gz/gK1/5Cg6Hg1OnTo3/3c1Qpmn2tuDZ7RbhcBexWEFmg8oSQ82AcrudA46x2VLD21OrByoBNdnOn7djWXDpkm3SE1CVlc/03n7hhe289NJOVr/nRu5be/sIjxq79CDyS5faJuT5JlPyp08S3vtvhNcd7d0WDu+g9X/+Dv5Vn9fqjzKi9Ap4GkAuIiIiIhNlTC14tbW13HrrrZSXlw/aV1hYyF133cWyZcuw2QY/XXl5Offeey9lZWUYhsGCBQu45ZZbOHjw4LCv9/TTT3PDDTdw55134vF4cDgcLFmyZBxvCzo7O2lubqa5efh2nOkqkUj9JdrjycXhSM2BSiVTZOAqeKltTmdfnjU3NxeHwyAU6iIW04eqyWZZUFBgYhhQWDi1k/Lj8TgAOa7cCXvO8vIiDAPa2gLE49DYaPDaay6amrKvm7n1lg9x6ncGV2q9+dEGatv/gYd3PkwwOr1mWclgpmly4MABOjq6OHTIwZ49Tpqbr/56bGlpIR43NIBcRERERCbMmCqgJpJpmhw6dIjrr79+2GMOHjzI6tWrefDBBzl79izl5eWsW7eOVatWjfl1nnvuOZ55JlUN8dRTT+HzDT+YeLpxOp04HE6KikqJxaKYZgKnMw+fT0vhpf9t8vLyKCjw4XK5KCzMx+lMVUEtWLCQs2fPEw53k0hYM+q6yEbJJCxcmPowvGSJA7d76l7bbnfidDrJz/dN2HleuHAhOTlOIpEubLY8jhxx0dFh5+RJi2XLsmtlvFOnfoJrbsug7UvyYL47QN2+Ol5peoXnP/k8hZ7CDEQoo3G5XKNeu2+//TavvPIKVVU3Yrd/lJISi0jEuuqfB11dXdhsLubOraS0NBd9q5yZxnKNiVwpXV8ymXR9yWTS9TV5pjwB9d3vfpeuri7WrFkz7DGdnZ28/vrr/PVf/zXV1dVs376dr3/96/zzP/8zc+bMGdPrfOQjH+HWW28d8JwzRSgUIhaL43J5iURitLW10NbWTWenZnQEg0Hi8TjJpEV3dyc2m49kMkFJyXGSJPH5avF624jHg5w7d4FFizId8cwWj0MolEr+tbcnprSSoqOjo+daSEzo17/X66KlpZuTJ5sxjAVEo1GiUZPOzuiEvcZE6OrajMs19L6NS+Bzu+Bw4DBff+PrbLp509QGJ2Pi8/lGvXbfeecdotEYp05dZPnyEF1dFvPnJ+jsvLoE1JkzZwmF4rjdeSSTncygH6HSz1iuMZErpetLJpOuL5lMur7GZzzJuintG3n88cfZvXs3Dz74IDk5OcMe5/V6uemmm1i+fDkOh4Pbb7+diooK9u7dO+bX8vl8VFRUUFFRMRGhZ5V0C15enh+7PdWCl0gYvXOhZrNIJAaAy5VKerRF2niq7YdU3vIMC973IzY2fp5fO/+LqC1CMNiRyVBnhf7XpDnFBXqxWOrrJH0tTJTS0tQcqAsX2li40GLJkgSlpdlVfRgKbcflOjTs/iV5sKpnbNzW+q3DHifZLRgMcu7cOWIx6O4OU1pqUlOTvOpZa6FQiI6ObhwON4WF+Tim/E9VIiIiIjITTUkCyjRNvv3tb7Nv3z4eeughiouLRzy+qqpqyBXyRlo1bzZJrYIHeXkF2GwQi3UBEItlOLAsEI2m5v64XC6C0SB3/fAuGhM/ZWWhSa0fqnKDvBT5Fb+a/wMudJzPcLRXJhTaTlvnK5kOY0zSSaeoGc5AAip9LUzsp+f0IPLz51ux2V4lwcsAWZUAHstqgBt7xuoFIgEiicgkRySTob6+HoBIxCAaDQMTk+i9dOkS8Tj4/SWM8LciEREREZFxGVMCyjRNYrFYb+VNLBbrGfZsDbqfTCaJxWIkk6l5KMlkkkcffZQTJ07w1a9+Fb/fP+rr3Xnnnbz11lvU19djmiavv/4658+fp7a29krf54yR+jdOfcLIy8sHIBJJVfJoEDlEo30VUJv3bOZw4DDrq/r2p2+3uwM83/kkiWnUtRiMBnl458O8cOhT/Orwp1n5xMqsHiQdjAb52q6HWbdrOfe+vZBbfnz9lMab/n6Vnv81UcrKConaImwNPMb2s2toiKxj3a7l/N2O7DkXc+du5Re/+B/8/Od/wb3bi9gzRFhdPdd+sacYj8MztQEK8Xicurpn+Zd/2cXp01f2t6AjR45gmhCN9iWgJiIRmloBTwPIRURERGRijak04NVXX6Wurq73/qc//WkAvvWtb1FWVtZ7H+Cxxx7jscce4+Mf/zj33Xcf9fX1bN++HafTyYYNG3qPu/baa9m0KTV3ZMuWLbS0tPTef+9730t7ezuPPPIIXV1dzJs3j7/6q7+irKzs6t/xNGeaJqZpYRh28vJSvZaxWHfP/w1gdn9YSLddud0uth7dyqoCqO2X86z1p1qP9rbDO9YrxONMi/aSYDTI2mfX4jHr+URPHjY9SPqlMy+x7Z5t+N2jJ3enSjre+rb63m2t0amNt68Fb2JPsNvv5MXKH7DQH+C6VA6YqtwWthys4zfN2XEugsEgpmni9/v5zMLbqfX/ZNAx6a+FW6vWZSBCOXz4MHv2nMBuP8+ZM+9lwYLxlbC2tLTQ0tICeDCMJIlEjEQijmnarzq2QCDQUwGlBJSIiIiITJwxfTK74447uOOOO4bd/9RTTw27b/ny5SPuB/hv/+2/Ddp21113cdddd40lvFkl3X5ns9nJyckDIBrtxLIsteAB0WjPIGhnktZIK39bM/iY9VXw5b0QMjrpikTxeqdwabYrtHnPZurb6vlmv4Ug0++jvq2eur11WTVIOh3vUKYq3ng81YLndE5sAurpc08TdAX4alXftkyfi3AijNfh7b2fSkxAYWEx75nTPOzjHlji5dYVD0x6fDKQZVm8+eY7OJ0W0WgIr3f837yPHDkCQFlZDV1dJzHNTqLRMJaVd9Xx9VVAlSgBJSIiIiITZkqHkMvVS7c22u123G4XLpcLSNLUFGX/fifNzbP7lKbn/vg8+dxW6htQ/ZSWrvzwJHOwW97BB2Shkaq5IPsGSW89OnI8UxFvOgHl8UxsC962k9uy4lykWzJXPrGSJf+xZEBLZiAQAKCs7DxO+1vDPkd1XhhXcvhh5TI5GhsbaWpqJTfXoqjIxO0evMpKPB5n//6DXLqUHDTXybIs6uvrSSZh7tzleDxePB6LaPTqZ62ZpsmFCy1YFpSWljDBHawiIiIiMovN7mzFNNS/Aspmg8LCQmw2i+bmIPE4tLbO7lPaN4Tcycalwy8Hub4KFrdfRzic/UOgwokwrZHWAbOs0tLbsmmQdDrekUxFvOkE1ESugpct5yLd4li3r45AJND7unX76lj77FpOXzoNQGHhT0d9rrEMLJeJtWvXO0QifTP7gsHBCaidO3eyZcvPePzxgxw8OLCK7/z58wSDQQwjj7KySvx+DzYbRCKhq54BFQwGiUSS5ObmU1CQ/dWhIiIiIjJ9zO5sxTSUTkDZ7XYMA0pKSrDbwbIukEgYFBVl13LwUy3W04fodu+izDl861GtHz7orqCjo3uqQrtiXod31GqubBok7XV4KfIUjXjMVMQbj0/8DKhsORejtTj+LPAzAEzzu0QiF0km9/Pss/+dH//474lELjJ/fjPV1U1UVzdRWfnMpMYqA7W2tlJffxKbzcGcOZUAQ34fOnWqkbY2g7a2ZpqaBs51Sq9+V16+DJvNRkmJF8OgpwXv6hajaGlpIR7XAHIRERERmXhKQE0z6Ra8dAVUcXExdjsYxiUWLUoyd64SUAA227+Oeux7bvgtF1ovTnZIE2K0aq51Ndk1SHpd9cjxTHa8lmX1roI30UPIp/JchELbCYW2D9o+Wovj24m3MQwDj6cYgPz8VEIskUitlNZTHCaA1XIBq+XClL3eO++kqp8WLVrBggWp89PR0TXgmGg0ypkzF3G7oavrEm53XyLIsiyOHDlCPA7z5i3H4bAoLEyd34mogErNf0Lzn0RERERkwk2D9b+kv4EVUBYlJSUYBnR2XsKyUh8sXa5MR5kZyWSSRMLEMOzk5Gxj3jwLn89HZ2eqvSWSiOBxeAhGg3zpqf/O612vEnXcTvGJYj5Z/Uk21G7I+OplQwmFto9azXVH+eopjGh0G2o38NKZl4as0qkprOGBVZM7+DqZTGKaFjabHYdj4vLsU30u0u1xOTm39G4bS4tj2BYmJz+HRCL1zcDvT7VSxeORnuScVsxMs+oPAGCUlE/6a4XDYQ4cOEg0arBs2Y3Y7UcxDAiFuojH6Z231NzcTHe3RW6uRSIRID8/SjRq4HbD2bNn6e7uxuHwU1IyF7/fJBjsq4C62hlQqoASERERkcmiCqhppv8MqHQLHqQTUBbx+NW1X0xnsVgMywKn04XdPvjfIZ18WvvsWl6KvEDUkaoG6T87JxgNTnXYoxrLjJ5ox5YpiGTs/G4/2+7Zxh8v3UiBI3WNFrqK2bBqA9vu2Tbpib54PI5lgcPhxDaB3+Wm8lyEQtsJh3cQDu8YUAU1lhZHr+mlrGgO8biBYUBOjh2n04lhmMTjMVVA9Ve/P/XfFNi/fz9dXQnmzKmioqKIwsJc7PZ0Aqrve9apU2eIxVL3HQ6LYLCFUCh1/8iRI1gWlJUtwzAMCgtNcnJy+rXgXV2M58+n/phRXFwya/+YISIiIiKTQxVQ08zlQ8jz8vJwu90Eg2HC4W5iMQ+5uZmOMjPSCaiRkg6jzc6p21vHpps3TWKU49d/Rs8jjzxKU1OSpJFgze9+lttuK5zQBMtE8rv9/NmKv+G+ogeJmRHmlLiYPz85Ja89WQmo9LkwTZN/+Id/pbvbzkd/7zNcf20uJ086yMkxqaycmPfYP9kVCHxjQBXUuup11O2rG/ax18Wvw+dLtXe53RaGAR6PB5uti1gsQiIxfBvhbGMdPTAlr5NMJtm9ezeRiMHq1aspLLQIhXzY7RahUBexGOTkpI49erQJgKKiPLq7W2lru0goVE5+foJjx44RjcLChcvxeCy8XvB6J6YCKhqNYhgHqKhwUF5eOAHvWkRERESkT5Z+dJXh9LXgObDZwDAMSktLsdshGLzU+1fz2SiddEhVQA19zGizc7bWj7w/k0zTJBZLla7YLQednUGi0QwHNYpYbDs22xu4bJ6rrswYj0Qi0fN1MrEJqLTU4OdCursNjhyI0taW+rrrv7LZeHV3d/P888+zY0cLr7/+NuHwjt59l1dBbajdQKW7csjnmWufy03Rm8jLKwXA47F6/u/BZrN6ElBXHOaMYP7sSZKfW0Pyc2sgcBECF3vvmz97clJe89ixY3R0dOH1llBRsYiCApPc3FxsNgiFOgmFdtDW+QqxWIzTpy9iGAY337wKp9MiGLxEOGyjsbGRcDiMx1OC319KYWEq2+T1eoGrnwEVCAS49tod3HTTTvLy9OuBiIiIiEws/YY5zaSGkBvYbDaMns+66UHk7e0tasGzwOFwYRiDP4WNZXZOIBIgkohMVohXJRqNDvhw2dUVzPqEYyTyTzgc/wgwpQmodKLO6XT0fp1MtPnzy4hEoL39JB0dNux2C9M06JmDP247duzgwIGD/PCHvyQn5+uD9re09FVE+d1+/qb8o3zCU4M7kUo+FDhK+Ny1G/gT40/w4CEnJ1UB1T8BZbenqmRSM6BmL9ua+zHuGTws3rhnHbY190/Ka6aHj9fU3IjPZ+FwgM/nI+GI8GvHz9lx9pP86vCnueHJG3gr/xXySn3U1MzH4YC2tkuEwwZHjtRjmjBnznIMw8Dv70tApSqgQle1Cl5r68uUl5+ltPQEdvsbE/XWRUREREQAJaCmnctb8CA1B8puT80JudIPvzPBaG1XY5mdU+wpxuPwTFKEVyedYEtLVUBlbyIhFNpOMrkDmy1VBTW1CahUiY/DMXldxsuWVVNZaREOH6SoyOxN9FxJFVQkEuHQoUPEYlBWtoe8vMEr30UifVVQlmVR5n+Ojy/q5uMnN/Cp41/m+7X7+MKSvyYSjGAYBl7v4ASUYUAsFtEMKMC48dbB21YP3jYRmpubOXfuHJblZdGi5b2Jo26zm2dLf4h36U6u9yeo9cPCnCCHit7mp0VbcPtd2GzQ1XWReDzOkSPHiUQMFi5chs9n9g4tT8+ACgQiHDvmmAsV1wAAIABJREFUoLn5yn60m+Z/9N4Oh//5qt+3iIiIiEh/SkBNM/1b8NJVPiUlJdhsEAyOrwJquCXep6v+Q8iHa7taVz246mHA/pqR92fS5RVQnZ1tV10BFQptp63zlauMbGj9Zxg5HP+YoQoo56S9xsKFCykv9+B2X8TpvHBVCahDhw4Rj8ex2fJ53/uGrzy5cCH1bxoIvERhYSNlZWdZtKgVB3a6uto5f74N0zTJz8/vXQHP7b68Aioy6yugAKxdr4OvgLZP/D+cuWsTCW8B5tuTU/XzzjvvkEjAwoWrcLudFBSkzsnmPZsJ2FtYX9V3bPr2ueRZ/u3Qv5Gbm4tlRTl6dDfd3TFyc+eQn1/Um8SC1LkF6OyMYJoWLS2j/2i3Wi5gtVzovR8KbcfjOdJ7PxrdMaN+PoiIiIhI5ikBNc30VUANbsFLVUCN/VN+IPCNMa3qNV0MXAVv6GM21G6gprBmyH01hTU8sOqBSYzw6qTfn9udarm6mhlQwWiQh3c+zAuHPsWvDn+alU+s5OGdD0/IKoDBaJAtux4YMMPIZttOZ+yFq37usYpG+1rwJovdbmfZsmVAamWynjE8405AWZbF3r17MU246aYi5s49O+yx8fgO2tu309ra93VbW/sqbrdFZ2eQ8+dTLaaFhSUkEgY2m4XbnTouXQEVj6dWSpvtc6AAbA99i/j1t9Kx9DYa/uDbk9LC3NHRwbFjx4jF7FRXv4v8fKs3Qb716FZWFUBtv0Uha/2wqiB1+4kDT1BSUoLTCQcOvEkkYlBRcS02m9WbxILUteh2u/F6TRKJMAUFo08it+oPYNX3DWAPBAZXPM2knw8iIiIiknlKQE0zZs8SR/1b8LxeLz5fLqYZp6OjfUwfLIdb4n06S7XgGTgcw1dA+d1+tt2zjQ2rNpBLHgD59kI2rNrAtnu24Xf7h35gFkhXQBUWluFwGIRCHYTD41/yKhgNsvbZtbzRWMeK/FTbz3x3gLp9dax9du1VJaHSz+1P/nTQvpOdX5iQBNdYTEUFFMDy5csBqK+vx+1OnYvxJqBOnz5Na2srdruPioofjHr8wdNfAPb23i8ubiC/9Djd3UFaWlqxLHpXwPP06yZND6pOJlMzzmZ7Asq25n4MX0Hv/aTXT8ftEz//ac+ePZimSXl5Dbm5vt7B4emZdP2rn9LS21rCLRQUF+B0Wr2tkwsXXovfbw36Huf1esnLs5g3r5PS0jH8IaJ+f+o/0j8P3hx0yEz6+SAiIiIimacE1DTTt7qXY8AHkL6V8FrG1JZ1+RLvM0H/GVBDDSFP87v9bLp5E/9Y9gjrGv6cfyp/mk03b8rq5BP0JaDc7hwKCnxYlkUw2DHuZdc379lMfVv9kG0/9W311O2tu+IYN+/ZjMesH1DRkbbMF+bpg39zxc89HvF4EgCXa3ITUJWVleTm5tLe3k4w2AxANGqMq91wz549ACxYUEs8/lNKSpqx2X7DSy89xNe//pd87Wt/yb//+4OcPP84//fOas6GA4OeY9Hqn9MWOUt7e4B4HPLySoC++U+p26lsVCIR7vm/2vAu1909sf8msViM/fv3E4/DNdesxm63yMtLnROvw8ttpb4hv1bSVVAl3hLmlc8jPcqsrKxyQBKrv5ycHGw2q2fI/OixWUcPYB1NVUCN9DNgpvx8EBEREZHMUwJqmuk/hLz/6l4lJSVEInDwYBunT498WtPVT2kz5a/c6QTNSDOg+vP7c7FbDoLBjkmJx7KgtdWYsIHPqRY8A5fLjd/vx25PteGNd/D8aG0/W+u3XnGMW49uHbKiI63E/PkVP/douru7+dGP9vLWWzaam1MJqMkcQg6pVth0G97Ro/W0txscO+bg5MmxfWvt6Ojg+PHjmKaNyspV2GwWfr/FkiVL+PznP8PKlWswzbmEQiHq9v0rhe6jQyYsritKcNz9vwkGL5FIGOTkpBJQ6flP0D8BlaqA0iDylJdeSvDoo/vZvh1CoYlNQB06dIhIJIJlzaO9fQHRKAO+b29c6hv2seur4A+v+0NKS0t7hscb2O0raG83yM0dnOH0er3YbBCJhIZNQJk/e5Lk59aQ/NwaCFyEwEW6/u79A34eXG6m/HwQERERkcxTAmqa6WvBsw1IshQXFxONGnR1Bbh0aZgBSD2G+ov2TPgrd7oCaqQZUP35/fkYBnR3d01KO1J7u8Hu3S5+8xv3Fa9K1V//GVcFBX7sdouurvGthDeWtp9AJECkJ0kxHuFEmEp365AJkrQV+QmCnb8e93OPxfPPv8Zzz/2ad945QCCQSkBNdgsewNKlSwFobGykuztV/XThwhguQGDv3r1YlsWcOTV4vXkUFPS1VtlsNt797mruv//T3H33JzjsOTxicu+mqnfo6GgjmTTweAaugJe6rQqoobz88hucPfsiv/3tHpJJg8j4L/1hNTQ0AOD334RlQTLZ928eCm2nzNk87GNr/fD55e+mqKgIl8uFadopL68ZtuIxnYCKRsMDXqc/25r7Me4ZuNBC6y3Dx5A2E34+iIiIiEjmTW55gEy4gRVQfR8u8/Ly8HototGuIdsz0i6vfkpL/5U7J+eWSYl7KvS14I2tAiovLw+7Hbq7O4jHDRyOiV2mLZk0aG+34XRatLbaqKgY/7ym/lKVFOB0uikocPergDKAscXudXh5X3Eutf7uQfvSVVBno8V4HJ4hHj36c392sQMYOZvXGfwWft8d437+0Zw8eRa32yIQuMSiRakV4Fyuyf8WV1hYCEAoFKKkxCQUsg1YoWw4iUSCAwcOYFlQWfmunuca+LjKyiStrf8/e3ce3dZ93vn/fS/2hQS47yK1kVpskXYsOXblxPEvjZPG8mTc1HHSJD/31yyt6eYknZyejtp0Ju3knGnTyXJqKanrNHE8dTSKo0lsJ27aOk7sWLJjx5YUa6EkS6REUuIGAgSxL/f3x+UlSBEAFwEQYT2vc3oq4V4AF8AFnPvoeT5fFaenjg3+UN7i3jZvmv7GN0lEe2ZXwMtegNKT66UDCtJpDU07iaqC290P9BAOK/PetysRnalmNTToH1xjY2p221KKOsHxh6hovo0PfvCDDA8rxGI22tuzf7/mFqDyfbbK23aiPZXpcmz5P118e81OhjUzlZXruPnm/0x7u3LFv1dCCCGEEEJcTgpQZWZuBtTcUQ6Xy4XLpWG1TtHQkPvCYbGsj3IuQMVi+iyaxWKZ997kUllZicmkEQ4HicWYXcWsUDQNPJ40U1Nq3qLgUhkdUPoIngOzGYLBySVlfs31qXYrsLAABXoX1Bnuy7ptKU6kPsWf/CJ3hlRvdy+7O3ev+PFzmZ6eZnIyiMsFVusoLlcdUJoOKH11OYVoNEpdXRKrlbzfQcOpU6cIh8N4PPVUVbVisWTygQzNzemZQoCNT4wsXtzbfuPLHD78btJpBZNJY+7Lt80sh5dISAeU4dy5EerqpmlshKqqATRNIxxWqa5OLX7nJTCK4rW1ZrzeJK2tmfOire0JAGI/eBTzv/4AgKiSxq6pKLvuQ737I1RUVBAMBmlqaqKpCSB3ZSlTgArn7IAC0H79S6jwcOG3epme9tPx0iNsCl+gZscd7Np110whvrDFeCGEEEIIIUAKUGUnnU7PdEDNH8FzuVzU118gnR7Jed9c3U+Gcu+Cikb1ApTVal3S/m63G1WFUCi4aI6SkYGynPcmnYa6ujR1dWmqq6/8gi4Wi5FO6yN4VVVVmEwawaCfWGzpjzE19TxrKiZzbu/xwu0NN634GHt7enn2wrP0TfYt2Nbh7OKB7gdW/Nj5DA4OzXZ9BALjhELVQGk6oFRVxWazEY1GiccjQAWatnhXmhE+3t5+A4qiUFWVv+ixWHFve2w7m4feSVeXPn43N/8JFmZAXeur4AEcO9YP6O9VKhUhEJjA4agp2ONnvrM2TCYta2Hc8vbb0WYKUHZN/1FXbtq57OfSC1Ba3gwog/aFhzj7eoLnDu+nqeNt3K5GefuuXUXPTBNCCCGEENc2yYAqM8YInslkmleAcjgcbNt2iG3bfk4yx9XHUkY+yjnrIx7XKxBLXfnMbrfjcFhIJuNMT+evQE1MfGXZ783cldAikWXdNat43Ciw2fF6PZhMMD3tJxpdenHr0qX/ueg+samHV3yMXpuXA7sO8IlNvVSa9CKQixre7/kMX9v2ZNFWGuzvvzj753g8ytiYXmQr9ip4BqfTCejdJ8Ciq+BdunSJixcvYrHYqKvbAiwcv7tcb08vXVVdWbc1m5vZHtuOxaLh9eoB5Jd39FksFlRVJZVKkEwmpAMK6Os7C0B1tQOLBcbHh4hGFVKFaYAiHo/PFKAs5Kzt/PpFQqqVf3FtZV/lVjR3JdqrLy77ueaO4OX7bNW7P8KvTkTYv/8JJifjtGzaQtef/pUUn4QQQgghRNHJ/+IsM3pxSUFV54/gRSKHaGi4AEAo9Bzw/yy4rzHyceDAT3jppT46OjYyNHQKl6ueP/mTj1FRUd5jF8YIns22tA4oRVHweCrw+SaZnAwC1Vn3m9s5tpwOsblhwZHI0nOacpkbQm6zWaisdDExESEQCKFpjiWNHZ48+UecPn2ad77znbS17eDSJZXHvve3qGmVL3zhM1RULC08Ox+vzcsDnX9J7eFuXjt6krVtO7iu5j24zQkWGyFbqXPnhgA9/yuZjDMyoncClqIDCvSLf9Av/i2WxQtQR44cAWDt2usxmWw4nWlmJuRyMop7e4/sZV/fPiaiE9TYa7iv6z5ujNzIMd8xUmbwePQC1OU5Roqi4HA4SCRCxOMxEolr++d/dDTM2NgIFouJt7/9bbzwwgsEAoNAN+GwcsW/h5qmzculM5lyP953O/4T5yfHqa2tw/THf4723E+W/XxOpxNFgZaWfyWWuAT8Udb9zpw5x6OPPkMymaCubjN33fVuTEtZtUEIIYQQQogrJB1QZcYYwbu8A2pud04y+fWc949Goxw7dhpFUbj11u2YzRCJTC9rJbXVKpFYXgcUgMejL4Pu9wdz7jM6+uXZPy+1C2pqamreWF80euXv79wQclWFqiovqqrnQC1lDC8Wi3H27FnSaYWuri4SCQVFUXBa3QCEw8uY5VvE+HiQwcHT1HpVNO0sHk960aLMSsXjcS5dGkNRVDo79RXpkslMHlgpzC1AQf4CVCQSYfDIa1TGozQ39wAseUTTa/Oye8dujn7sKG/+wZsc/dhRdu/YTWtNKwAWi0ZlpV5IzRakbbfbUVU9ByqVUor2mZSDN944B8C6dW2sWbMGgMnJQQDC4Sv/vhodiyaTFVVVc3ZAqXd/hCB1+HwqNlsbSoUH9e4PL//5THEOup5n/ZaniPM3bHtsG196+Uv4Y35AL4gdO3aCRx99CrM5Tmvr9bzjHXdJ8UkIIYQQQpTMtf1P4GUoswqeitFRc3m2k6r+OmenTl9fH6FQmoaGdjZsaMBiUZmaihAOl/+KR0YG1FI7oACqqvQCVCCQvQB1+vT30bRfzf59KTlZJ06c4Omnf0JFxW9TX38zHk86b/fDUmXGefQClNfrxWwenlkJr2XRlbvOnTvHhQsaJlMHw8Pe2Qtim81ONBomFIoCzis+ToCjR4+gaRo1NWlMpnGczgCa5i7IY1/u0qVLxGIaVVX1bNjQyPHjx2a3lWoEzyhARaNh3O753W+Xe+ONN2gO+qiqrsViqUVVNTye5X//5q5U6PXqo43V1ZX4/XYCAZWqqhQu18IcKEWBZFIvlCUSsMTItLecvr5+ALZuXUd9fT0Wi4VgcJJIZJpQ6Mq/B0ZB3GTS3+B8tdD29nVcuDBAZ+fWFT2XP+bn/l/cz/XrTuGeeZ7baybYe3Qvz7z5DA+6/pzzfSOcPx8kGlW47rodvOtd71hS16QQQgghhBCFIh1QZcbIdzKZzLMdUNm6cnJ16rz++jHSaejs3IrbDRUV+oXz5GT2VdHKyXIzoAA8HjeKAsFgcEFw78mTJxkZ+V8L7rNYF9TRo0eJxeD06ZNoGgQCKqmUsmjQ+WJisRigYLFYUVXweDI5UEtZCW9kZIRAQCUeX8t3vuPiN7/R3yeXS5/9ikSiV3aAM+LxJCdOHAUyBb7R0fNF67YZGBginYaGhhba2+cHSFsspenuyBSg8ndApdNpjhw5QmvYz9qZAkVlpcaVNqE0NzfT3d3NO995G2azRlVVikBg4c+7EUSeSkVR1RcJhXIvSvBWFgymGRwcQFVh8+YOVFWlsbERiwXGxoYL2gFlNusFqHxF6Ntv38QDD/TS3V2/oufac3gPp/ynuL8jc5vx53Ohc3zr5LcYH59C09x0d9/Bpk1WUspzK3ouIYQQQgghVkoKUGUmswqePoKXa2U7o1NnromJCfr7L2Gx2Lj++g0AeDwuAPz+8i5AaZo2e8Fnsy29AFVZWYnJBOFwkEQic9H5xhtv8LOf/SP19RcW3Cfbe2sIBoMMDg6STCrEYhdJJCKz3S1XMoZnvL7MCJ6G1+vFZGLJK+GNjIxjs2n4/Y2EQgpvvKG/TxUVelFC74C6cm+8cYpoNEJ9fQM33ngDigKXLp0vyGNnYwSQt7U1UVdXM6+YU+oOKH0VvNwFqHPnzhEIBGiLTuEZ6QcWDx9fClVVefe7382mTZvYuDE5s/LiwsfNFKAimM1fxu9fWGC9Fpw4MUQiEaOpqQaPxwNAS0sLJhP4fIOk0wrRK/w6xGa+lKqqF6DyZXw3N6e57rokzc0rOxf2ndrH77aAe85zuM3wuy36n8/XnOTjH/8wn/7s7/HmuqcJ1fwuZ6L3cd+vt8wb0xNCCCGEEKKYpABVZjIjeCYUJX83zuXbjh07Riym0N7eRU2NfqXi9RodQKGCrfx0NRhhvyaTBbN56ae12+3GZNIIh4OzRZzDh4+wf/+/sXVr9iIT5H7fT548CehL3K9f34+39ns0N+tv7JUUoPTXp2EymWfzv/QClDaTAbX4Y1+6NIHLpbFxYw0ul4bTqY8GOp16B1SuAlQ4fDBnwW2ueDzO8eNn+MUvfkUopODxvA2brQMAR+JfMYWeXvoLXqJ0Os3588MAdHS0YLfb8Xgyo37LKUZeibkjeJC9AJV+8nE6/ufn+OzJX1ARj2CZGmXrP7wP55/uIv3k4wU7lnzFDKMAZbW+gqoeJJHIXUzN5pVXLvGd75zgwoXynt06ebIf0LufDC0terUmENAD7cPhK/vP4+UjeIUYw80mkozgi/rmdT8ZjNumtSnq1ip89sh/5lToIa73JOnxQodrnL1H93LPU/dIEUoIIYQQQhSdFKDKTGYEz0Q0mr37yTC3U0cf/TlBKgUbN26dzYZxOp2YTBCJhMo6iFzvDsqMpy1VZWXlTCeZ3gH1yiuv8oMfPEtV1QWamgZz3i9XF1RfXx9Rohxt+DVttz7BVOXn+eDLW/jW+b9mZCqwkpcGZLopTCa9WJTJgMp0QIXDucf8IpEIfv80JpOFHTtcbN+eoLs7idUKTqdelDAytC43MfGVnAW3QGCKQ4eO8M///H/567/+Jt/5zlNcvDgJVLBu3SYUpRGHw87Wrc9iUv92xa8/l4mJCcLhOC6Xh/p6vfBUX58Zwyt9BpTRAbXwu5R87wd5qbZ9we3KrvtQ7/5IcQ9whlGA8nr/9+xtSw3WB/jhD3/GCy/8G08+ebjgx1Yq4bDCwMBZVBW2bFk3e3tTUxOKojA1dYlkMnHFY3hGR+ZSOqCuhMPs4GPtjnndTwajC6rGXsMjbzxC32Rf1jG9vsk+9h7ZW5wDFEIIIYQQYoaEkJeZuSN4k5OLXzhOTHwFp/NW+vv78flCVFTUsHFjM4qid0cYHUCRyDTRKDgLk0FdcpnxNCuKsvROA7fbjdmsF6AOHXqJl1/+JYmEwjvfeWLR+xrvrcHn8zEwMsB+935aqsbZVqXf3u6c4ImLD/Fq4N/5cdsBvDbvil4f6ON3oBegHA4HTqeNiYk4wWCEQ4cqmJ5WeNvb4rS2zu9+8fl8JJPg8dTQ0AANDcmZx9NwOPSiRDgcWfC8c0c8w+GDOBy3MDx8iTfeOMvJk+cYHh6b7fZRFIW6uia6utbT0rIVq9VETU2arq4IFS2jwCih0EFcrtwB7ss1ODhEMqnQ0tKM06kfSH19DSdPDgBgtZbmJ84588XJlwEVCAQ4VVHH28cH5t2u3LSz6MdnsNvt1NRcwOU6PnvbUoL1Del0EEWBkyef5/jxJrZsWVlm0dU0MOAnGPTh9dpobm6evd1ms1FbW8vw8BgTE5dwuVqv6Hkyq+Dp39liFaAAPromd/vq/R3QWHsf3+v7Ht0e6Jnz89PjhW4PHAnAvr597N6xu3gHKYQQQgghrnlSgCozyaR+oWEymWhre2LetmPH/Dz66Ldpbq7ks5/9w8u2HSMaVbj++uvwejNXx0YBJhKZnumAKs912Y0RPIvFsqxAZ6vVitNpIxiM88tfvoiiqOzceSebN3+OmaYWfvOb3/DMM/9GRcX13Hbb73Dddcmsj9XX18evbL9iXB3nLzoyt9/fAZ87Av3hPvYc3stf3Lz8izyjA8pszhSgAKqqvAwNjREM+gkEPGganD5tprV1fjfT2NgYiYSC11uL2ayRTOrdHRaLhsulF6CyhZDP7Y45efIveeaZDxEIhGdvM5uttLW109W1lq1b11Jf75yzspb+PgUC/zHv8QpZgBoYuIim6eNTxgV+U9PcDqjS/MQZHVCxWO4ClN/vp3NqjJjVzvi7P0c6rdD6yz1or76Icveakhyn3W6ns3Nh1+TlxdRs9BHQMNXVAGkOHHiGlpaP4PGUpsusUE6cOAdAV1f7zGqiGc3NzYyOjjE+PkRDQxupFCsOiM8UoIwOqOL8tvp8/4RVzb3CgdsMv7/Gxp4jPr7QtXC78fs0EZ0gmozOW11RCCGEEEKIQpIRvDJjjOBlyzmanq7A51MZHQ2jzbkCjkQinDx5hnRaYcOGLfOWZne5XDMdUG+FETy9ILKcETwAj6eCUEhhctLMtm13ceedm2eLT5AJKo9GAznDidPpNCdPnuSY5VjOLgPQuwxWIhaLzXZ4qao2W+QxcqDOnw8wOang9ytomkLksmamsbFxUimoqqqlsjLz+c8dwYtE5ieZXx5w73b34XSexOWqZNu2G7j33nv4r//10/zxH9/FHXdspaHBOW9Z9/STjzP91+/CZPrN7G3R6CGm//pdBcs86u/X83ra2zOdLE1NtYRCCn6/ldHR0q6CF4no371cBSiAl9/7cfzr38F0105Mf/1QSY7PYLUep7Z24WhpJHKI0cC/5b1vNBolnda/Y/X1NUxN+XjiiZ+XVXZcJKIwMHAOVYVNm9Yu2N7S0oKigN+vLz5wJWN4pRrB8/kW74RNTD/CbXUV836XDMbvU429RopPQgghhBCiqKQDqswkk/poldm88MI6EnFgMlmZmooRj8ex2fRumZMnTxIOazQ2dtDc7EZRMleMLpfrsg6o8jR3BG+5Bah169bw618H2b79LrZsWY/NNr/DyVglKzYT0hsOK9jt8ysMR44cYcQ3QqQykjMM+HNHwBdbWZdBPB4nndY7WOYWeYwcqJGRABs36oWp2to0Q0MmNmzIfM4XL04AUF9fi9WaOXZ9BE+/QI5eVlnLlg30/vefpaPjK1iti58r6t0fwbfmGwtu992dxN1z5ZlHwWAQn28Ki8VOS0um66m+vhaz2YXdXsnUlApc+Spzi9E770xEo0mSyQSatvCn1e/3c7Sugx1ePeza4dBQKjwod3+46MdnSKYeybntV28+wM6tr+YcEY1EIqTT+rjhhz/8O3zzm4/T1/cGzz/fwbvetbFYh1xQY2MJRkYuYLfDunULC1DGSN7k5BCaphEKKVRUrKxzyfjOms22eUXjQtuwYeG4cLbfmAdj24Fg1se4vwPOcF8Rjk4IIYQQQogM6YAqM3NH8C7X1pbCZnNhMkEoFJq93Vj9bt266/B45l+Mzx3Bi8eVnMvHr3aZEbzlZUAB3HHH7fz5n/8xt966noaGhcUKPahcJZGYJpVKEonM/9oEg0F++ctfYsHCLdWuvF0GVdaVdRnM74DK3K53QAFMoijg8aQxmzXCYRWfT7/i1TSNkRG9ANXUVIPFknl/bDbmjOBl2qYu737KeJVkMnfw/Vzh8EGizrMLbo86zy5r5bVchoeHSSQUamubcLszV/dms5nPfvZ+PvKRe6mtLX7xCfT8K6dT7wCLxfRCzeWMDii7vWrm/5f2yxYOHwTtSM7tne4I3z/2Fzm3RyIRNA1sNgdNTbXs2vUOFAWeffY/OHcue2FjtTl58gLpdIr29sbZrrW5KisrcbvdaFqUQGD8ijug0mm9OFnM/KdsLv+NCYcPUm8Zzrl/jxc+0XVTsQ9LCCGEEEJc46QAVWaMApTFsvCKZt26FO3tdmw2bTanZ3x8nKGhEVTVTnv7Btzu+Re9TqcTVVVIJCIkk6mcq6itdkYBymxeXgaUob1dzbl0vaqqVFRUYDanmZ4OLLgo/dnPfkY8Hqe1dQO96z05n+P+Dnhf48q6XYwClNVqm/f6jBG8+vpf0bHh36irS9PUpJ8jFy+aSCZhsv8A1fYjWK12qqtdWK2Z++sZUI55zwH5V0Zb6qpphXiMfM6fHyKVgoaGlgXFnLVrLbztbUrWz7NYHA7HbAEq3wie1eqd2b+0BailvOe16adzbotEomga2O0OVBW2b+9m27Z1xONRfvCDfyUUyrzX4fBBJoPPFeS4CyUSgf7+syhK9vE70AuJLS0tWCwwNjZMJHKQUGhlxdJEIjFTgLKVvAB1uaV89rGph0twJEIIIYQQ4lomI3hlJjUTuJItA0rvgHEyMgJjY2HWrs2Ej7e3b6Kmxjxv/A704orT6cRkChONhonFHNhs5dcGFYvFZvNpljuCtxSVlZVMTgYIhQJEozWk03oQ+JkzZzhz5gwWi4WurkqczvxdBmvtO1b0/HNXwZv7+hSnwksVz/Mv3lz8AAAgAElEQVT/bn2NM9G9/Pdjtfx+5F7e7/kMwbDKX/7i6+yo+AYVN6fZ9/rPMZ3y8WB3L2NjDUxNKdTWpvB69bG+eDxKMgmJRK7uJ91SVk3L3UG19MdYzMDARQDa2pqLNt60HEZHTTQaXlCASqVSBINBFEXBYvGSTEKWBpyiqm16jA3f3rDIXkne15N9RHR6Wu+QM16noijcc897GBx8jNHRQZ5++hXevauLb/5mD1tMD5NIJ/kffTV8qPND9Pb0rmj1x0Ly+xWGhs5it2usX78u537Nzc309fXh8w1iMv0T4+PaioLzjd8ki8WKyXR1f1MvX7ACso/pCSGEEEIIUUzSAVVmUim9yyDbCB5AdbULgImJCKlUihMnTuQcvzPoQeTlnQOVSCQAZUUZUEvh9XpRVT0HStMgGlWIxWI8++yzAGze/A7s9r2LPk6F9ZvEYovutsDci1ljxNAf8/Oxn32MVOOrbKtK0+OFDtc4e4/u5U+O3MV/OfZ+jk7s4Tqvvq2zappHTuzlQz+9h3B6kpqaND6fis2mF7X0ApS2pG6JxfYpxGPkE4/HGR4eQ1FU2tsbV/w4hZTpgAoD879HgUAATdNwuytIJi0oCiUv9DrMDqrt1Xn3yRdEHQrpBSinM1M5czgcfOhD78ViUTh0+Dne/8QuXhzYy9bKJD1eaLVNsPfoXu556h78MxlqV8u5c+NEItPU1Lipq6vLuV9Li57RZTa/gKoeJBY7tKKRUT2XTsFstl71DqhspPgkhBBCCCFKTQpQZSaRMFbBy16AqqlxAuDzhTh37hyBQAins4b6+sacYbpGDlQ0Wr4r4c0dwStGAcoIIk8kMkHkL774ItPT09TUNFJf/zbi8R/S0jJMZ+cQnZ1DRKOjjPtP8eSTn+fHP/486fQA8fgPiUaX/x5nQtbts69vz+E99E32zQs9N/58duoMF6Kns27rm+zjoOnrVFenqa5OYzKZsNmsaJpGJBKnre0J1q0b4Ac/+DMeeeTzwLnZ12T8X7aOirna2p6Yt//Pf/5tHnnk84RC/7rkx8jn0qVLxGJpqqrq8XgsK36cQnI69e9etgwoY/zO5dLzn2w2rSjn6WLu68wfNH3vxtzbQyE9pN7hmF+46OhYwx133Mixqpe4EDub85zbe2TxAm2xRCJw7ty5mfG7DpQ8LXN1dXVYLBY2bsysCriSYmlmBM+K2Vx+XaVCCCGEEEIUmhSgyoimaaTTRgdU9o+uuloPQp6eDnPkiBE+vhWPh5xjSnoHlEY4PM1lC6GVjfmr4BX+Yu/ylfD6+y9x+PBhVFVl8+b3oigq1dVpXK7Mc4+NqVwarMXhaCKdTjM5eR7Ql4Jfrrkh5Ebz275T++j2MC/03Ag7B/Ju+/HQ9+ZlXhlFBaPIMDIyQjicwuOpobb2yjslbDYjZ6owJ9jg4BDJpEJdXQtO5+q4uM+M4C3MgAoEAjP76B9IqQPIDb09vXRVdWXd1u7YxMfW9ea8b6YDauH5cPvtv0W/93jec25f376VH/gVCgRUhobexG7XWLcu9/gd6GPJGzZMU18/OHubMTK6HLFYjFRKL4qvxg4oIYQQQgghSk0KUGUklUqhaaCqJkym7EUMp9OJ1arh949z8Y3jWCKhvON3oBegjJXwyrUDKrPkeXE6oCorKwGIxQKk02mee+7fiWtxNm68CYejEbNZmw3/NgQCKpoGHo8eeDw62g+wog6oWCwGKFit+rhcJBnBF/XN6zYxGLfl2zYRnSCazBSD7Pb5Bajh4WHicYW6ulZcrisP8rZabQBEF6lwHj58mKeffppXX4Wf/czK8HD2D3NgYBhNg5aWZiyrowFq3gje5QWoy1fAK3UAucFr83Jg1wF6u3vxWvRKkVNz8rG1n+LLW56ESO4RvXBYL0AZofVzxbU4YSW0rHOulC5dijAxcRGnU2XNmjWL7t/evjBAfbldUJmVOW1XPQNKCCGEEEKI1UD+XbaMzC1A5SqyuFwuNFuU/2Af5+uPMm2O8diJ7/MRPsSDN2QPAna73ZhMEItNk0opJJOU3b/Y6xlQC0O6C8Xr1d+3idBF/v5ILwfdPyFmjuAZe5zf5sP86Y4HMJnmr4DX0xNnYkLFZGpjZARGRgZYu3ZlHVCZJd1tKIqGw+zgtroKerzBBfv2eOF3W+Z3oszd1u2Bwdj8rB+7XS8QRSJ6QFV/v17gaW5uwWZb9uEuYLHoDxLLE4CladrMWGOUwcEbqKvbwMhIesFKdul0mvPn9QDy9vbmKz+4AjEKUNk6oC5fAe9qdUCBXoTavWM3u3fsZv+B/Vw4d4HOyBYqzF6CQW02YP9y4bAxgrewALXY+ZjtnCuVaBT6+/sBjXXr2rDOXQYyi3D4IBbL8QW3Lzc4P/OdXZ0ZUEIIIYQQQpSadECVEX0FPAWTyZRznC5pSfKjmn0cr36FabN+sR9ITvCN3+QOAna59ODyRGIaoCy7oIwRvGJ1QDkcDlKWFI9ZHuO5+A+ImfVukEBygicuPsQfPP+fF7y369al2L49QXd3A1arlakpH+HwFImEQiqV7VlymzuCZ7y+BzdW5Nw/WyfK3G33dc3P+pk7gqdpGv39eoGno6NpeQeag9Fhla8ANTExQTQaJRZTiMUuoCjZO4XGx8cJheK4XB7q6twFOb5CmJsBlbsApXdAXc0C1FzvueM9mEwmTp06ztTUedJphamp7N//SEQvQLnd2ZfvW+x8vPycKxV9/O4sNlv+1e8M+TqdltoFpWnaTAFKkQKUEEIIIYQQM6QAVUaW0gH17dPfxmeeyLotVxCwUYCamAhx5oyZgYHsAeerWTyud0BZrZacxbkroSgKr7teZ8K0vPcW9BUL16xZg6LAxMQ5YPljeEaBzWq1YTLpXRr1luGc+7vzXPD2eOETXTfNu83I9YlEYkxOThIIhLHbXTQ1VS7rOHOxWvXHzzeCNzQ0BEAsBvH4eTZsSGYNzh8eHiaRUKira141+U8wtwMqPC+EPJ1O4/f7SSb1EHKzWVs1Y4Ner5cdO3YAcPjwv3PypIn9+x28/vrCEyjfCN5i52O2c65UfD6Nixf7sds11q5dm3ffcPggkcihnNuXmgWVSqVIp9MoigmTySwjeEIIIYQQQiAFqLIytwClKNkvaPa/8Vjex8gWBGx0bkxNRdE0PTy73ESjcUAvQBXLa9prebfnC1k2cmfGx/uB5Y/hZTqg9BHDlazKNe/xph6e93en0xjBizI0ZBR4WqjI3dSyLEYGVL4OqMHBQdJpSCQUJiYukUwmCIcXnovnzw+RSkFDQ8tVy1LKZm4G1FzT09Ok02msVhcWi3XVdD8Ztm/fTkVFBeHwGMePDxCJKJw7N78Apa+QqBegHI6FM5lLOR8vP+dKIRqFwcFhEokojY3VVFVV5d1/Ka9jKfvE4/rvkarqv0erpeAohBBCCCHE1SSDAWXEKECZTOasXT6RZASfFsr7GEYQ8NwsFttMyI/FEkFRyBtYvlrFYvoFn60QgUVZRJIRprXpvPtke28N7e3tAIyODqBp2rI6oJLJJKlUarabQlXTtLU9sWC/XM+92DbIdECFw1HOnfPN5D81s0hczpIZHVD5ClBDQ0PE4/q+6XSUiYmLWCxrFmQSDQzo44Gtrc1F6XZbKbvdjqIoxONRUqlMkckYv3M6r24AeS4Wi4Wenh5eeOEF0ulXgPXU1s6fEY1G9ddktTqw2UzA/NdgnI+TkwqXjk2QTqf5+YmnGXhzks7OG/jYx24vSJbYck1NLW/87vLv1Usvmdm//18IBi/x8Y/fzG23vX1Jz5sZCbahqlpRxoKFEEIIIYQoN/I/i8tIaiY4SFXVrBc0DrODanvuVawAauwLg4BtNhuKomCzxVi3Lk5dXfkVoOaO4BXDSt9bQ1VVFZWVlSSTYXy+kWV1QBndFHoAuYKqZi9g5CswLRb+7HJlRuQy+U+FC/hebAQvEAgQDAYBBx0dm7HbNfz+C2gahMOZ9yoYDOLzTWGx2GhpqSnY8RWCqqqzWVqxWGT2dr/fP7NC4+rKf5rruuuuQ1VVrNbTbN8+Slvb/N+ASCSCpinYbA5MeSZ0zWZwDR2l4uIb7HrfXTidCqdOvc7Ro7nH84pJz396E5tt8fG7bNasSfO2t70Tq1XjuedeJRTKX+A3xONxUik9sy3f+yWEEEIIIcS1RApQZWQpGVD3deYP+s0WBKwoClarFUWBRCKGpq2itpIlMgpQNluBWnayWMl7a1AUhfb2dsxmuHixn1iMBUHVuRhFm2jUzpkzZkZHC/+1NQonk5OTjI76MJnMtLfXFezxMyN4esj55YaGhtA0qKxsoaGhDZtNL0ABhEKZ81HPf4K6umbc7tV3nhorxEWjYSKRKAcPvsIzzxwimRwixQBjY+qqLEA5nU66urqwWtOcPn2Y6WmVZDKzPRKJkE7rhcR8eUZmMzgHj+IcPEp9fS23376dUEjhe997gQsXSvufm1gMxsammJqaoKLCSktLy7Ifo7k5ze/9XiObNq1jejrBs88unv8EmQ4oCSAXQgghhBAiQwpQZWSxETyA3p5eOk0NWbd1VXXxQPcDWbfpXVAa8XhsXoByOTBG1FTVhMVSvHaD3p5euqq6sm7L994a2tvbUVUYG+snnVbIM402z/S0PvqXSFSiafpYUaEZHVAjI3r+U01NEx5P4Z7HbLZgMplJpdIk51Y2ZgwODpJIQG1tG62trZjNMDk5TDqdnleAGhoaIpFQqK1tweVafYUcIwfq8OFf8uUvf4v/8+R/8B88w8Y7vo/3+v/OA+c28b+OfinrapRXW3d3NyYTnD9/hGQyOe88i0ajpNMs2gFlMmm4ho7iGjqKyQQ7d96MolgJBC7R1xfOfccimNv91NHRjmmFrUhWK/z2b9+Goqi8/PIxxsbGF72PvgKe0QG1+s5TIYQQQgghrgYpQJWRxUbwALw2L//3939Gb3cvNXZ9RKnGXkNvdy8Hdh3Aa/NmvZ9egNI7oMqtAJVIJGZGnCxFzVrx2rwc2HVg2e+tQV8JT8HnG0Sd/iHj53+4pOcNBAJoGlRXe1AUaGxMLX6nZTJCyNNpDU2DlpbmggcnWyz6c2QbwxseHiYWU6ivb6W+3oHX6wXiTE6OEg4rs91iAwPDs/lUqzHY2eFwEA4rHD36JhfGgzzX8QMsa19hW1WaHi+srxznG0f3cs9T96y6IlRzczN1dXUoSoTz5/uYnMwU/vQRPL0Ala2jJ/3k46Q+eTem3ruxBkexBkdRH7gb5Y/v4fc9IygK+Hz9pXsxGAUoPf9p3brF85/y6eysYvPmbhIJjZ/85IVF9zcKUGazdEAJIYQQQghhkAJUGcl0QOVeBQ/0QsnuHbs5+rGjvPkHb3L0Y0fZvWN33gKJHqAM8Xh02QWocPggk8HnlnenApo77lLssN+VvLcGh8OBu87NK57nOGX+Iw76e9n22Da+9HL+jphAIEA6Dc3NlWzalKC1tfAVQofDzsaGM6xr6AOgo2P540qLyRVEHolEmJiYIJ22UF3diMej0draiskEPt950mmFaFT/nIeHx0mpKdasaSz48RVCS0sLyaRCU1MXse3TTJgucn9HZrvx577JPvYe2Xs1DjEnRVG44YYbsNk0+vpeJxRSSeiTrYRCxgp4jqzdl+rdH0HZlWW8d9d9KLs+QHV1Gp/vXDEPf554HILBJKOjA9hsrCj/aS5VhTvvvBmLxcbx4/28+WZ/3v31orgyM4InHVBCCCGEEEKAFKDKSiYDKvcI3uUWC582GB1QyxnB88f8fOnlL/HT4x/m3098dEnFlGJIJBKAMjPmVbrnXep7a/DH/Hwr/S1ofYXrvHpHTKttgr2LdMQEAgGSSXC5KrFaC38x64/5+dqxr1G980dU3/YUT6zdw1OR7xT8c7TZsheghoaGSCbB42nGYlFxubTZvB6/fxCAS/4p/vIXf8k/1zzEvvVf5QMv3nBVzrXF3HTTTfz5n/8J9957F79O/4huD/TMqU32eKHbo/95X9++q3OQeXR1deFw2AgGh/D5RggE9P9EGAUop9OR877K23YuvO2mnXR26qOng4P9hEKlKcYEAiqXLp3HZErS1NSIy+W64sdsanKwffsONA2efvoF0nl+KGOxmHRACSGEEEIIcRkpQJWRuR1Qhe70yXRALS2E3B/zc89T9/DiwF62ViaXXEwphlgsNtMBZcvbGXa17Tm8h8H44LI7YgKBAKmUgtvtxb68mteijM/xpQv/yPVVGj1e2FQT4dE3Hy7o56gomRG8SCQyb9vg4CDxuD5+V1GhoSjMFqB8vkGmEpN8/Ocf4F/6/4WoSb/vZPzqnGtL0d6usmFzEH/cN++zNhi3TUQniCazrwp4tVitVrZu3YrNpnHq1Ov4/UYBSj9OI2Q9G+3Xv4QKD+qn/wz1038GFR60V1+kqqqK2loP8XiUc+dGS/I6jPwnu31lq9/l8u539+ByVTI0NM5rrx3Pud/cDCgpQAkhhBBCCKGTAlQZyXRA5c6AWqlMB1RkSR1Qew7voW+yb1WMF5UqA+pK7fvNoyvqiDE6oNxub8E7oEr5OWZWwlvYARWLQV1dCxUV+snn9XpxuVykUmH+d//fcXa6L+tjrsZRNgCH2cFtdRXzPmuD8ZnX2GuW3UVXCt3d3djt0N9/nMnJKLEYhMNGB1T+41W/+BDKTTtRbtqJ+sWHAH20b8OGNQD09Q0U9+DRx+9CIbh48U1sNq44/2kut9vMu96ld3r99KeHiMfjWfczfpMkhFwIIYQQQoiMVXy5Li6nh5AreVfBW6nljuDtO7Vv1YwXJRIJNE0fd1mtBahIMoJPCy27IyYejxMOhwETTqcbm62wF7MrLYot19iYysSEm2BQmVeAisfjXLo0SjJpoq6uhcpK/fUpikJLSwtmMzw3+UT+17AKR9kAHtxYkXPb/R1wX9fCzKTVoLq6mo6OdszmBGfPvkEgoM4ZwctdgFLv/ghKhWf270qFB/XuDwOweXMHAOfODZAqfIb+PFNTKn7/GInEFG63i4aG7KuCrtStt3ZSX99IIDDNc8/9Ous+elemIiN4QgghhBBCzLFKL9dFNpkOqMKP4M0tQC12gRhJRvBFV894USaEvLQZUMux0o6YqakpAOx2D4qiFLQDaqVFsZUIBlXMZhuhkDJvFbyLFy8SjabxeuvxeCzzLtZbW1tJKgmmtUDex16No2zh8EHqLcM5t/d44RNdN5XwiJanp6dnZgzvCJOTCpGIXjR0uXKP4OXT3t6K1aoyNjbMxERs8TtcAb9fYWjo7Oz4nVLgar3ZrPDe974DgOeff5VAYHrBPolEgmBQ4dIlJ6OjBf7XAiGEEEIIIcqUFKDKyNwCVKGzjowCVCIRXTQDarWNFxljMBaLbdV2QMHKOmL8fj+aBg6H/mbbbIU7nlJ+jk1NSSwWG2bz/BG8oaEh4nGFhoZWKivnt961tLRgwYIj7cz72KtxlG1i4iuL7hObergER7Iy69ato66ugulpH+fODTA1tXgIeT52u522tkY0Lc2pU4OFPNR5EgkIh1UuXnwTq/XKV7/L5brrWtiwYQOJRJKf/OTQgu1656KCyWRhamoV/ygJIYQQQghRQjIcUEbmhpAXcwRP00DTyPkc4fBBPttpyflY93fAGUo3XjQ3A2q1hpAvpSPm9oaFHTFTU1OkUuDxeLBYtIIX2PSiWDDrtkJ+jmvXpli3zorfr80rQA0ODhKLKdTVtc7mPxlqa2ux2Wxsjm7hNeers2OBRy5riFqNo2xtbQvHBqPJ6KorlOWiqirbtm1jaOggp069TiCgd5i5XCs//q6uDt58c5jTpwfYuXN9oQ51nkBAJRoNEwgMUVWl0t7eXpTnAbjrrp18/etnOXz4GLfd1kNra93stlgsjs2mYbHYqKtb4rKiQgghhBBCvMXJP82WkdTMbFwxRvDsM8urJWdGmbLlQPljfr708pd4oe9DVJt9OR+r1ONFxgjeas6AWmlHjL4CHrjdnoLnP5VyTMzp1LBabSQSmRG8VCrF+fOXSKehubmZyxdYU1WV5uZmbk7swBOr4f4OFowLdlV18UD3AwU5xmIrl+KT4frrr8fpVBkcPEM6rWG12rFaVz7j2tm5BkWB8+f7iUaLUyj2+xUuXuzHak3T0tKCrZAtg5dpbq5i+/YeNE3jySdfQNMyrykSieNyaWzapNLcLAUoIYQQQgghQApQZaXYGVCgd0DBwgKUP+bnnqfu4cWBvax1LX5BVcrxonLIgGpre4LOziE6O4dIJP6N/fv/Cz999h9xOi/N3p6ta0ZfAU/B7fYWdPwOSjsmZrOB3W5F02B6Wj/HRkZGCIWSVFbW0NCQfcyutbUVh2Ln/9Nuo8c7fzSwt7uXA7sO4LVlmSEUV8zlcrF168bZ3xqbzXFFK7o1NjZQWWknFAowNJQ/12sljPG74eHCr36Xy5137sBms9HfP8DRo5kV/qJRfSzY4cjdKSqEEEIIIcS1RkbwykixR/AAksnsBag9h/fQN9nHV7sztx32w+eO6H/e5LuJP+v5U+68852FPbAlSCQSgLLqM6AMTqcTRdFIRhMkEvn39fv9pFJQUeEteAdUqcfEvF79cYNB/RwbGhoiFlPo6Fg4fmdoaWkB4OaeV2dv23vzzXS0HyjKMYr5brihh1de6SMSUWYKUCt/LFVVWb++ndde6+PEiQHWry9s4XBqSiWdTjM+fhanszQFqIoKB7fffjM//enzPPPMC2zdugazWSUa1b/YDoe16McghBBCCCFEuSiDy3VhKOYInlGASiSyj+DtO7WPbg/zAquNbhSAc5XHCIcjhT2oJYrH46s+A2oup9OJqsL4eIQTJywMD2f/MDVNY2pqimQSXK7Kgq6Al0sxx8SqqvSLcaMDamBgiEBAIRZbQzCYvaLa0NBAXd0QDQ2Z4Op47GXC4YNFO06R0dzcTGtrLaGQwuSki7GxK/vh2bRpDQBnz/ajFfh09vsVxseHUZQIXq+X6urqwj5BDu94Rzderwefb5wXXjgGSAeUEEIIIYQQ2UgBqowkk3oBylSEOTOr1YqiKKRSiZlOq0xBIJKM4Iv6FuTvQCaTJ2aOEAgVfqxmKRKJxKrPgJrLKED5/RGSSfD5sh90JBIhkUhgMtmx2RwF74AqNa83U4BKp9P09+sdUM3NrUxOZn8PzGYz11//6wW3L2V8UFw5RVG45ZYbSSQUXK4qAoEra73csKEDsxkuXrxAIJAq0FFCMgmhkMrw8NmSjd8ZLBYzv/M7OwH4+c8PMTUVJR5PAlKAEkIIIYQQYq4yuFwXBqMAZTYXvgClKMpMF5RGIhGb1wHlMDu4ra5iXveTweiCsiUdJCKLzJMVSTlkQM3lcDhQVVDVEKmURnV19vEzv9+PpoHT6UVRwFrm0zwej17kjEQSXLx4ienpGNXVHmpqKnO+B+HwQVyuMwtuj0QOSRdUiWzdupU/+IPf5Y473k5Dw5UFarvdbhoba0mlEpw5c7FAR6ivfgcwPn4GVYW1a9cW7LGXort7I2vWNBOJhPjxj18inQbFqmK1FnhWWgghhBBCiDImBagykilAFedjs9lsKAoLClAAD26syHm/+ztg/dT1hEKhohzXYuJxfdylXDKgzGYzdrsVhyNFa2uIxsbsF/WZ8TsPVqtW8NyvUjObFZxOfdTz2LEzpFKwYUMb3d2JnCuF5et0ki6o0tC7oFq59VZTQVZ027hRH8M7fXpgkT2XLhBQCIWmCIdHsVgstLa2Fuyxl0JRFHbt2kncFOWfL3ydx5r28r/b/47bf3wdX3r5S/hj/pIejxBCCCGEEKtRGVyuC0MxR/AgU4CKx+cXoMLhg9RbhnPer8cL77Y1Ew6H5y1FXirxuDGCZymLAhRkxvCi0TDJZPZ9AoEAqRS43Z6S5D+VgsulF6COHz9NMqnQ2NiO3Z79tYXDB4lEDuV8LOmCKk+bNnWgKNDf379oCP9SJJPgC8Zmx+/a29sxm0u/voan0cXz6w9wvOpXxEx6Hp4vNsHeo3u556l7pAglhBBCCCGueUv6X+kvvvgiP/3pTxkYGCAWi7Fv377ZbT6fj0ceeYT+/n7Gx8d58MEHecc73jG7PRAI8Nhjj3H8+HGCwSBer5c77riDD3zgAyg5Wjr279/PgQMHsFgy+Rl33nknH/3oR1f6Ot8SUim9KlSsApTdbp8pQEXnFaCW0mlyy42v8fzz64hGozgcjqIcXy7xuH4Va7WWT96KXoCaIhoNk0p5gIVFGL0ApeB2F34FvKvF7bYyOgrj4/pra2xcw0z+/QJLOe8mJr6C03lrgY9SFFNbWwsOh5nJyVFGRsK0tjpX9Dj+mJ89h/ewr+//4ItN4NTcbHFs4rfW3FLgI16aPYf3MKJlL9T3Tfax98hedu/YXeKjEkIIIYQQYvVYUgHK5XLxnve8h3g8zsMPPzxvm6IobNu2jbvvvpuvf/3rC+4bjUZpbW3l3nvvpa6ujgsXLvC3f/u3WCwW7rrrrpzPuXXrVr7whS8s8+W8tRkdUBZLcf51f34HlIJRFGlt/T5f+9p3uXjRx91338POne1Ek9HZFdPOnzfxi198C0WZIBKJlLwAFYvpq6rNLViudnoBSiMWi5BIQLa3LBAIkEzqHVC5ijTlprJSfyE1NRdwuN1UVjpR1ewtYG1tT5Ty0ESJmM1m1q5t5dixfvr6ztPaumnZj+GP+bnnqXvom+ybvS2sTPOq7VV2n97ND7f8EK8tS2hdEe07tS//9r59UoASQgghhBDXtCUNLPX09LBz504aGhoWbKuqquK9730vmzZtQs0y/9TQ0MAHPvAB6uvrURSFNWvWcOutt3Ls2LErP/prTGYErzhzZkYH1OUZUEND41y86MNud3DjjXq2ilF8AlAUsNudgEI4HC7KseWSTqdnV5yy28snpdsIIl/qCN5bpQNKcSZ5vfYXtN36BJgpKRYAACAASURBVNXd/8zvvrRFMnKuQZ2dRg5U/4ruv+fwnnnFp7lOB06z98jelR7aihgrheYzEZ0gmoyW6IiEEEIIIYRYfUqemJNOpzl+/Djt7e159zt16hR/+Id/SG9vL9/85jeZmppa1vMEg0GGh4cZHs6dXVRuUqniFqAyHVDzR/BeeukUAFu2bMTpXDj+Nzqq4ve7CQZLX4BKJIz8JysmU/mkdM/NgEqlFh53Op0mGAySTCpvmQwof8zPV/1fxdLxCtuq0vR4od0pGTnXoq6utagqDA31s5KfjKV0G5WSw+yg2l6dd58ae828wr0QQgghhBDXmpIntX73u99lenqau+++O+c+t9xyC3fccQc1NTWMjY3xrW99i7/7u7/jb/7mb3LmRl3umWee4Ykn9BGe/fv3U1GRexW3cqGqZiwWCx5PZVFeT1VVFTabFVUFp9NNRYXG9LTG6dPnsFot3HHHDVmfNx434XZXMTamdyCV+r22WKw4nS4qKlxUVMxfpctqta7Kz76mpmamYyuF3a6/13NNTk5iMpmprPTg8VRSU3Plq49dbV9+/csMJYb4fEfmtvs74HNH9IycR048whdv++LVOrwVWa3n12rndrtpaqphdHSKkZEQ27Y1Lvm+kcTSuo0sDktJCz4fv+7jfO3Vr+Xefv3Hl32uyPklik3OMVFMcn6JYpLzSxSTnF/FU9IC1KOPPsrrr7/OX/3VX+F05g6ebWtrm/1zfX09n/70p/mjP/ojRkZGaGxc2oXK+973Pnbu3Dn792AwuPIDXyVCoTCJRIJ4PFqU15NKpUgk4kxNBQgGQwSDKV56aYTJyTHq6lzU1VVlfV6324yiWDCZ4oyPj5f0vfb5fMRicTQNwuFpgsHUvO0VFRWr9rNPJuMEApP4/SEqKuYf9/DwMOFwHKvVQTIZIhjMMadXRr77xnfp9uirJhp6vNDtgSMB+O5vvsvnez5/9Q5wBVbz+bXarVnTyNDQBK+9dpy1a13Lum+1vTpvEarGXkMikiBBAZbZW6JPbvkkz7z5TNbRwK6qLj6x+RPLPlfk/BLFJueYKCY5v0QxyfkliknOr+VZTrGuJCN46XSab37zmxw9epQvfvGL1NTULOv+S+16mquiooLm5maam5uXfd/VqrLyDPVN5zCbi7MKnj6Cp82O4E1PKxw71oeiwI03dub8HJqb06xfb8Pp1Eo+gheP68Uni8VKkRYHLAqn04miaDkzoOYHkJf/+J2RkXN/x8Jtxm2SkXNt2bSpA4D+/gFSqfz7Xu6+zvvyb+/Kv70YvDYvB3YdoLe7lxq7/t+4GnsNvd29HNh1oOSh6EIIIYQQQqw2S+qASqfTJJNJkjNXyvF4HNBXHVMUZfbvmqaRSqWIx+OYTCZMJhOpVIp/+Id/YHh4mP/23/4blZWViz7fyy+/zObNm6msrMTn8/HII4+wbt26rCHo1wJjufGujY+ypjPNfb9+jo+E76O3p7egFzXzV8GD4WGFgYGTuFwaW7Z05byfyaRhtztJp7mqGVBZMvBXLafTickE0WgoawaU3+8nlVKoqPC+JfKfHGYHt9VV0ONd+C8JRhfUYEwycq4lHR1tWK0wOjqEzxenrm7piwj09vTy7IVnc3YbPdD9QCEPdcm8Ni+7d+xm947d81YKFUIIIYQQQiyxAPX888+zd29mVaGPfvSjADz00EPU19fP/h3gG9/4Bt/4xjf44Ac/yL333ktfXx8HDx7EYrHQ29s7u9/mzZvZvVtfkvrhhx9mfHx89u8vv/wy//RP/0QsFsPtdnP99dfzyU9+ckWdUOXOWG7cnu7j93r029qdPvYe3cuzF54t6L+sz10FLxBQuXRpgHg8xJo1nryjj2YzswWoSCRSkGNZKr0DSsFstpRdAUpRIBaLkMgyJTQ1NUUqBS5X5VuiAwrgwY0VQPZW1vs74Ayl71oRV4/D4aCtrYk337zIqVOD1NWtW/J9jW6jv3vxHzg+8m3iphgXotV8eNOHeaD7gVXRbSTFJyGEEEIIIeZbUgHq9ttv5/bbb8+5ff/+/Tm3bdmyJe92gE996lPz/v6Zz3xmKYd1TTCWG/9qd+a2ucHNe4/sZfeO3QV5rrmr4AH095/A5dLYtKkrb/HPZAKbzYmmlX4VvMwInj4+WC4cDgeqahSgFgaM6yN4Cm63F5vtKhxggYXDB6m35F6RsscLtzfcVMIjEqtBZ2f7TAFqgN/6raUXoEAvQn3AfR93mp7GZrfy9ptfKNJRCiGEEEIIIQqhjHpGrk37Tu3LGdwMhV1u3ChATU7GOXVK4dSp0zgcGps2bcp7v7kjeKFQqGDHsxTGCJ7FYimrDChVVXG5HGiaRjSqZ27NFQgESKWYKUCVT2Etl4mJryy6T2zq4RIciVhNOjvbURS4cKGf6Ariv8bHf0xT0yDVVWcJhw8W/gCFEEIIIYQQBVPSVfDE8hjBzV/IEr9kdEEZwc2FGPewzbTaBINxxsbOkUhEqaurpba2Nu/99A4oB6ASi8VIpVKYSlQNMjqgyi0DCowuqNhMELkV60wETjweJxgMoygW3G4XZvMyE5pXoba2JxbcJhk5oqmpkcpKG4GAn6GhAOvXe5Z8X03T8Hoz3bUTE1/B6by1GIcphBBCCCGEKAApQK1ipQ5u1ruIVMzmOMPDb+B2pxftfgK9AKWqClarA00LEg6Hl7UU45XIhJCXVwYU6DlQquqfKUBVzYaNZ/KfPDgcb93cMyk+CVVVWbduDa+/fpqTJ/tZvz4za6xpGj/60Y948cV+JidV7HYNlyvTDdjYOMD73z8w+/dI5BDh8EEpQgkhhBBCCLFKldkl+7VHD27O7v6Owi43rigKdrsNl0sjHj+J263R1ZV79bu5TKZf0tx2oeQr4WUyoMq1A0qbKUBlbs+sgOd5S6yAJ0Q+mza1AzA5+e+EQpkxuoGBAc6ceZPJSWVmVFUvShn/d+ONC0fuljLqKYQQQgghhLg6pANqFbsawc12uw2IoWlpmpqa8Hrzryblj/nZc3gPm9WHqe5O8vevOxg7com/eOdflGQlqvkjeOVVrNE7oFhQgNIDyN86+U9C5LNhQwdmM6xZ8wNGRw+xdq3ewfTyyy8Tj8ONN95Ge/st9PTEaW7Ww9LC4YMMDf39gseSLighhBBCCCFWrzLrGbm2XI3gZr0ApevszN/95I/5ueepe3hxYC/XeZL0eKGzKsLjA49zz1P34I/5C3ps2cwNIS+3Dqj5BajMqF1mBK9SOqDEW15FRQWbNvlpbDxPIvHSTHFpiMHBQdJpOzt2dHPTTQlaWjQURUFRFHy+r+Z8POmCEkIIIYQQYnUqs0v2a0tb2xN0dg7R2TmEw3HL7O0Wyy2zt2cLd74SDoeey6MoChs35s9/2nN4D32TfdzfkbnN+HPfZB97j+wt6LFlk8mAKr8RPKMAFYtFFnRApVIKVpcDu8QkiWtAd/eLs3+emPgKr7zyCgAdHTdgtdqprMwsExkOHyQSOZTzsYwuKCGEEEIIIcTqUmaX7Nemyy+4EoniXWDZbDZCIQXoIBiszLvvvlP76Pboo4AGIxwdYF/fvqIc41yxWAxNU8oyA0ovQOkZUKmU3gHlj/nZN7GPfW17+MzIu7n5iev50stfKkk3mRBXQzh8EIfjxOzfI5FD+P0/J502U129g/5+E5OTmS/3UjqcpAtKCCGEEEKI1UcyoMpAtoupYi05XlVVRSym0NV1PYFA7hXYIskIvqiPL2SZ0ru/Az53BCaiE0ST0aKudja/A6q8xtX0EHJ9BC+RyIw09qX7Zr+ZE9EJ9h7dy7MXnuXArgMlydUSopSy/b51dh6iouI/EY1WUFmZwudTZ/OfCt31KYQQQgghhCiNMusZufbkGjcp1pjJjh07+NSn7uWWWzppaEjn3M9hdnBbXcW87ieD0QVVY68pavEJ3joZUKmUMjvSmE2pRhqFKKVcv2+1tYNs3WrD40njdmtUV+f+LRJCCCGEEEKUhzK7ZL/25BslKcaYidVqZceOJrZvT812HOTy4MaKnNvu74D7uu4r8NEtpI/glXsGlL4K3r5T+UcWSzHSKEQp5f8N+yr19Wm2b08s+lskhBBCCCGEWP3K7JL92rKaw3bD4YPUW4Zzbu/xwie6bir6cWQ6oMqvAGW32zGbVeLxKFOREL6oL+/+xkijEG8Fi/2+qepB7PYXMJlKeFBCCCGEEEKIoimzS/Zry2oO213K88amHi7qMWiaRjyeAMqzA0pRFJxOB4oC6ZhGtb067/6lGGkUolSW8huiqn9fgiMRQgghhBBClIKEkK9ibW1PzAZT29N9fK1Hv/2zh+FIALqqujiw65GrdmxzTT/0dQKxFI8lLFgscXp7e7Hbi1ssSaVSpFJpVNWCxaIC5Tem43A4UJQIsViYD66/j4eP5c55KsVIoxCl0tb2BANTA9z+/dvZ1RTnwQ367Q+dgR8MgUWx8tNdspqdEEIIIYQQbxVl1jNy7TGCqe/vyNxm/Hk1BVNb+39D9dBxHA4nAOFwuGjPFQgEmJqaIhKJk0iA2Vx+AeQGp9OJyaQHkX9q84O0WFqy7tdV1cUD3Q+U+OiEKK7en/UST8ez/r4ltDj/5VDv1TgsIYQQQgghRBGU6WX7tWPfqX10e5i32pyxyhxc3WDq9JOPk/rk3aQ+eTemwCjW4CgPvvEjPnvyF5h+8v2iPGcqleLxxx/n8ccf5+WXE5w9ayYWs5d1AUpRNGKxME7Vy+c8n2OrfwcVqv6B19hr6O3u5cCuA/z/7d17fNT1ne/x928yk8zkOpBkgpBARIjgBaLF61JrfXihpxaRKqvI6bK6C1txPb1te/Rx1EIfnipupboSeqjUVpY+UkvdCttFrduK5aKgFVAuEUQQyjWXmVzmPvM7f8QJpCEhl/nNJMPr+Xj46Mzv8v195pdPUvLJ5/v9uXPO8MhBYAjbdnKbvjpKyj+tFzffLn11VGL/++kJDAAAAEDSMQVvEAtEA2oMNuqRC7vum1spfXP7qYWp07E2kG36bMUlmWs7F8HeLh4jz1U3qMiCa/r9/o7uqm3b9so0pUgkZ8gWoFwul2y29g6oWMyQGTB1Wf11+t7l/09XTy1UXg5rPiEzNQWbZMrs1P2UMLeyfRqeKVO+oE9FTit+mgAAAABIpSH6a/u5wWV36fOlBZ26nxISXVDpXpja+NzULts+Kiy1bApeYlzTlI4f3yHDkAoK7LLZTEuuZ7Xc3NyOAlQ0KrW2tsk0JZcrj+ITMtow57Au3U8Jp3dBUXwCAAAAMgMdUIPcA+MLJLWccd/cSmmf0rswtfneBqmgSObfztfhw3aV/vePNb75pOUFqGhUMgyfrpqwTyPGbFckPkLSdZZc00qnF6DCYVMtLW2SpPx8V5ojA6x33/ndPzxgbqX0u2NOeUNepp8CAAAAGYAOqEHM798kj+NIt/ur3dI/XDglhRGdmW3hc7JfNVWBi6bq99f+L8VN6xYhT4zbFg/q3ZL/lvvzv9Woi1/TB967Ne5n4/TIpkfkDXktubYV2gtQpoJB/2eLqsfkcOTI5cpOd2iApRobfypXVvdPrsy3S18eERw0D1oAAAAAMDAUoAaxhoazP4I81Lw8BZF0zzZ9toyC9ikyTqcpW2Gp3iqotLQAFVRQLw+rlbPyfU0e1v6LarVbqsoP6Gc7f6bb1tw2ZIpQp68B1dLSplhMcjrzZLcPzSmFQG81Np7959vcyvQ+aAEAAABA8jAFbxCrqFjdZVu6FhzvDZfLlNOZq2jUsKwAFQgEtCVni7z2Rj1W2XlfYmH2fd59qtleo4evfNiSGJLp9Cl4ra1+maYhlytPDke6IwOsNW7cbgWiAY17YZyWTFaXte62edu/n6X0PWgBAAAAQPLQATXEDOZfwk4VoNoLRVbw+/2qs+/Q5KKuv7AmFmaXhk7XRKIAFQoFOjqgXK48ZWXRAYXMNxQetAAAAAAgOShAIWmczlMdUG1tbZZcw9vmVUtW+IyPbpfUsb0h2N41MdhlZ2fLbrcpGg3L5/MpHm+fgkcHFM4V7Q9aOLO5ldJdF6b3QQsAAAAAkoMpeEianBzJ6XTKNA21tQUVi8WUlZWV1GtEA1FdWZitanf4jPsTXROHQ0Oja8IwDOXl5cow/GpqqqcDCueU3jxo4fqy9D9oAQAAAMDA0QGFpMrNNZST47JsGp7f79c/jun5CXFDrWsiMQ3P6z2haNSgAwrnjKHwoAUAAAAAyUEBCkll5ULkpmkqJ2e3xg1v7fG4arf0DxcOna6JRAGqublJ8TgdUDh3VFSsVlXVX+QZs1PPH1ygD312bfNKd7xTrF833S/PmJ1nfBgDAAAAgKGHAhSSyuWScnLaFyJPdgEqFApp/PhNvTt2CHVN5ObmyjBMmWZckngKHs4p3pBXM9fO1IdNS3VJUVTVbqk8p0E1O2o0c+1MeUPedIcIAAAAIAlYAwpJ1d4Blaf6+uR3QPn9fm3ePEux2DBNmzZfFRVRDRtmdiw2PhTWfDqT3Nxcnb5UlsuVJzvfmThHLN22VHVNdVoy+dS2uZXSN7dLdU11qtleo4evfDht8QEAAABIDjqgkFQ5OaZyc/MUi0ltbcktQCXWlHI4cj+7Vvt2p905ZItPUqIDqv11+6LkrvQGBKRQ7Ue1mlzUPnU2IfEwAUmqratNT2AAAAAAkooCFJLKZpMKCtoLKF5vchchT3RU2e2JAlRmrJPkcrlk++w7MSfHpZwcvi1xbghEA2oMNmpuZdd9iW0NwYaOLkcAAAAAQxe/6SLpioraC1A+X/ILULGYlJOTr6wss9O0taGsvQDVXkxzOlmAHOcOl92lz5cWdOp+Skh0QRU7i4d0hyMAAACAdqw0g6Rzu60tQDmduRnT/SSdegqexALkOPc8ML5AUssZ982tlPbprpTGAwAAAMAaFKCQdIkCVHNz8hchj0YNOZ25cmZQQ0SiAHXeeZ9qxIgsOqBwzvD7N8njONLt/mq3dH3ZlBRGBAAAAMAqTMFD0g0f3l6AamlJfgEq0QGVnZ05RZpEAeryyzdp3Li1dEDhnNHQ8PRZjwk1L09BJAAAAACsRgcUkq6goL2gEgj4FQyacjqNpIzb3gGVeVPwHA6HRo48qvPOOyxJisU2SromvUEBKVBRsbrjdf2PnpUkfXrT/bp8Ev/XBAAAAGQaOqCQdNnZ2XI67YrFompujiRt3EAgoFjMyLgOKEmqqtrc8ToY/FEaIwHSI+8vO5T3lx3Kzc6g+bUAAAAAOvBnZliioMClkydb9fbbYU2Z4tTIkfEBj9nWdvoi5EkIcpDw+zfJ7T7Q8T4c3iy/f5Nyc69NX1BACsTX/FLm2lpJUvZn28b/6H8oJsn4yl2yTZ+dttgAAAAAJBcdULBEYWGhQiFDbW3NamwceJrFYjG1tgZkGIby8pwdT43LBGdaB6c3a+MAQ51t+mwZX+n6lDuKTwAAAEDmyaBf4zGYFBYWKifHVCDg0/DhA+9+ap9+J+XkuORyJWdNqcHA79+kQGBzl+2BQHsXFJDpjM9N7bptStdtAAAAAIY2ClCwRGFhkcaNO6gRo/6YlOl3fr9fw4cf1siKwxm1AHlPnU50QeFcYL63QSookm3+d2Wb/12poEjmuxvTHRYAAACAJKMAhaTzhrxaG3hJFdeuVuuwxzVp5SQ9/s7j8oa8/R7vX7f/q0ouW63hk3+mW964ZEDjDRbddT8l0AWFc4Vt4XMypkyVMWWqbAufS3c4AAAAACxAAQpJ5Q15NXPtTB2IvqRJw+KqdkvlOQ2q2VGjmWtn9rlolBjvw6ZVHeONdvV/vMGkNx1OdEEh09mmz5ZRUNTx3igokm363WmMCAAAAIAVKEAhqZZuW6q6pjrNrTy1LfG6rqlONdtr0jreYFJRsVpVVX/p8b+KitXpDhMAAAAAgAGjAIWkqv2oVpOLpGr3qW3VbmnyZw0OtXW1fRrvg6Mr9dVRyRsPAAAAAACkHgUoJE0gGlBjsLFTt1JCYltDsEHBaLDX480c2ZK08QAAAAAAQHpQgELSuOwufb60oFO3UkKia6nYWSyn3dmr8czw+6p2S/n25IwHAAAAAADS4wy/2gP998D4AkktZ9w3t1Lap7t6PdbZFuDu63gAAAAAACA9KEAhafz+TfI4jnS7v9otXV82pddjBQKbezymL+MBAAAAAID0YQoekuZsHUuSFGpenrSx+jIeAAAAAABIHzqgkDQVFas7XgeD0p/+dEy//+NKXXZpue6+++5ej9NT95PnvFVyF1w/0FABAAAAAEAK0QEFSxiGlJ9fJFvcLp/P16dze+p+avE+N9DQAAAAAABAilGAgiVsNsnlypdh2NTW1qZoNNqr88629lMgsFl+/6ZkhQkAAAAAAFKAAhQsYRiSzWZTbm6BJKm5ublX5/Vm7aferg8FAAAAAAAGB9aAgiVsn5U28/OLFI83yefzafjw4Wc97/R1pLZv36dVq9aqvHycrr9+piZOjMjhsCpiAAAAAABgFTqgYAnDaP/f3NwiSb3vgDpdONw+ba+k5GNF9QdlZSUtPAAAAAAAkEIUoGCJRAEqL69Ipqk+L0QuSY2BRr1fvF7Z457WvuBdql41SY+/87i8IW+SowUAAAAAAFaiAAXL2Gym8vMLJfW9A8ob8urRT74tx/lbNckdU7VbKs9pUM2OGs1cO5MiFAAAAAAAQwgFKFgqP98t0+x7AWrptqU6HP5UcytPbUu8rmuqU832mqTFCAAAAAAArEUBCpax2U5NwetrAar2o1pNLpKq3ae2VbulyUWf7a+rTWKkAAAAAADAShSgYBnDkFyuPNlsNrW1tSkSifTqvEA0oMZgY6fup4TEtoZgg4LRYNJiBQAAAAAA1qEABcsYhmSz2fq8DpTL7tLnSws6dT8lJLqgip3FctqdyQwXAAAAAABYhAIULGP7LLsKC9sLUH15Et4D4wu63Te3UrrrwrsGEhoAAAAAAEghe28O2rhxo1577TUdPHhQoVBItbWn1t9pbGzU888/rwMHDqi+vl4PPPCArrvuuo79Pp9PK1eu1K5du9TS0iK3260bbrhBM2bMkGEYZ732kiVLtHnzZi1atEgTJkzox0dEuiS+vAUFRTp6tPcdUCd8r8vjONLt/mq3dMWwickIEQAAAAAApECvClB5eXm6+eabFQ6HtXz58k77DMPQpEmTNH36dD3zzDNdzg0GgyovL9esWbNUWlqqQ4cO6cknn5TD4dCtt97a43Xfeecdtba29uHjYDAxDFOSoYKC9pXDe1OA8oa82vLx/arK7/m4T48v0vmltychSgAAAAAAYLVeTcGrrq7W1KlTVVZW1mXfsGHDNG3aNE2YMEE2W9fhysrKNGPGDHk8HhmGodGjR+vaa6/Vzp07e7xmS0uLVq5cqfnz5/fyo2CwSXRAJdaA6s0UvKXblmr+ewF9cb30jW1d939jm/TF9dL/fLshmaECAAAAAAALpXwNqHg8rl27dmnMmDE9HrdixQpNmzZNHo+nX9dpaWnRkSNHdORI91O5YK1EPbIvHVC1H52a3vkvF3bdn9gWM2M8BQ8AAAAAgCGiV1PwkunFF19Ua2urpk+f3u0xW7Zs0YkTJ/Tggw/2+zrr1q3T6tWrJUkvvfSSCgq6X9Qa1sjPNxSLGSotHans7GyFQqEevw6BSECNwUZJ7U+6G+XqeswoV/u+7T7J4XKowNnz1zU7O5uvPSxDfsFK5BesRo7BSuQXrER+wUrkl3VSWoD6xS9+offff1+PPvqocnNzz3hMa2urXnjhBT300ENnnNLXW1/60pc0derUjvctLS39Hgv94/dnye+3KRaTotGompqa1NDQoOzs7G7PGe4crsZg4xm7nxL+5UJpzhYpK5KllkjPX9eCggK+9rAM+QUrkV+wGjkGK5FfsBL5BSuRX33Tl2JdSgpQ8Xhcy5cv1969e7Vw4UK53e5ujz148KCampq0cOHCTtufeOIJ3XTTTbrnnnt6dc2CggKqlml2qn5oqKioSE1NTWpublZJSUm359xVdZc2Hqw5Y/dTwiiXNHP02KTGCgAAAAAArNOrAlQ8Hlc0GlU0GpUkhcNhSZLD4ZBhGB3vTdNULBZTOBxWVlaWsrKyFIvF9G//9m86cuSIHnvsMRUWFvZ4raqqKi1durTTtq9//eu6//77dfHFF/f5AyJ9EouQm6ahwsJCNTU1yefz9ViAWlC9QF/IXS4p2uPY/3wB6z8BAAAAADBU9KoA9dZbb6mmpqbj/Zw5cyRJzz33nDweT8d7SVq2bJmWLVumO+64Q7NmzVJdXZ02bdokh8OhBQsWdBw3ceJEPfzww5Kk5cuXq76+Xg8//LAcDoeKi4u7xFBYWKi8vLz+fUqkhWGYkiTTVEfh8WwLkbtz3CpzumSaPbc82tSanCABAAAAAIDlDNM0zXQHkQo8DS/1jhyxqb4+S+edF9P+/Zu1YcMGTZkyRV/4whfOem48Htdjj/1YgUhc/+d/f0eG06ciZ1GfY2D+LqxEfsFK5BesRo7BSuQXrER+wUrkV9+MHDmy18f2f5Vv4CwSa0DF46c6oHw+X6/OTUz3zLa5ZBhGv4pPAAAAAABgcKAABcucWgNKKipqLyCdbQpeQqIAZben9EGNAAAAAADAAhSgYJlEASoe73sBKhKJSJKyshyWxAYAAAAAAFKHAhQsk5iCZ5pSbm6usrKyFAgEFAqFFI/3fG6iAyoriw4oAAAAAACGOgpQsMypKXhG+zpORUWKxaT16/367W9d+uijrG7PjUQickcDGm4GUxQtAAAAAACwCgUoWMYw2h+wmHjOosNRrIYGmz76yCvTlA4f7r4AFY1GVRn2akyoMRWhAgAAAAAAC1GAgmUSHVChkLRvX5akETJNyWY7KsOQ8vLMbs+NRCKqDHs1OngiNcECAAAAAADLsMAOLJMoQPn97XXOkpJSFRXF5XYfDtdyrwAAFYJJREFU0bhxUWVnd1+AikajGhP2KSvWlopQAQAAAACAhShAwTJZWdLJkzb5fDaNGRPVlVcO186dktd7UqZpKhIxZJqnClWSFF/zS5lrazW2Y4Ok73xFMUnGV+6Sbfrs1H8QAAAAAAAwIEzBg2Xy8005HKZGjYoqJ8fUsGGFysnJUSDgVyTSItNsn553Otv02TK+cleXsSg+AQAAAAAwdFGAgmUMQxo3Lqq8PFPDh8dlGIZKS0slSa2txyVJ4bDR9bzPTe26bUrXbQAAAAAAYGigAAVLjRwZ1yWXRDVyZFySVFZWJklqaWlfXDwU6lqAMt/boIgzV6vyLtZbE2Yo6iqS+e7G1AUNAAAAAACSigIUUirRAdXc3F6AOlMHlCRtu+1efZjt0V/KLtXBv/tJyuIDAAAAAADJRwEKKeXxeCRJXm/7FLxgsOsxtumzFbRnS5LsdrtiuUWyTb87ZTECAAAAAIDkogCFlBo+fLhsNptaW70Kh4NnnIInSZFIRJKUleXo9JQ8AAAAAAAw9FCAQkplZWWppKREWVmS13tS0aiheLzrcdFoVLm5h3X0aJ327LGnPlAAAAAAAJA0FKCQcmVlZTKMU0/CO1MXVCQS0dVXb1R19Qs6ciQr1SECAAAAAIAkorUEKZdYiPzUk/Akl6vzMTk5u3X++YckHVIotF7SlNQGCQAAAAAAkoYOKKRcYiFyn6/7DqiSkt91vB4z5snUBAYAAAAAACxBAQopV1JSIklqbq5XLBZTONy5AHXC97oKCj4+bcsmLX/vfnlD3hRGCQAAAAAAkoUCFFIuJydHbrdbhhGTz1ffqQPKG/Jqy8f3dznHHXtFM9fOpAgFAAAAAMAQRAEKaeHxeJSVJTU1nVAodGr7Sx8+rKr8QJfjq92SM16nmu01KYwSAAAAAAAkAwUopEWiAOX1nlAsZigabd9eYv6u23PmVkq1dbWpCRAAAAAAACQNBSikxV8/CS8cNtTU8kddUhjt9pxqt1Se06BgNJiSGAEAAAAAQHLY0x0Azk2JJ+E1N5+QaZoKBqW2tqVnPe8fx9rltDutDg8AAAAAACQRHVBIi7y8POXm5ioWC6q11adQyFBFxWqdiIzs9pxtXml3fH4KowQAAAAAAMlAAQppYRiGSktLlZVlqqnphMJhQ37/JnkcR7o9p9ot/cOFU1IYJQAAAAAASAYKUEgbj8cjuz3xJDxDDQ1Pn/WcUPPyFEQGAAAAAACSiTWgkDaJJ+G1d0BJVVWrJUler1crVqxQIDBM0279O026KEvZ2WkOFgAAAAAA9BsdUEgbj8cjm03yeo8rHjcUDrdvj0bbn4RntzuUbXPKMNIYJAAAAAAAGDAKUEgbt9s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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_date='2018-06-08 8:00:00'\n",
    "e_date='2018-06-08 17:00:00'\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(df.loc[s_date:e_date].price, marker='o', ms=3, color='b', alpha=0.2, label='tick')\n",
    "\n",
    "plt.plot(minute_df.open.loc[s_date:e_date], marker='x', ms=3, color='k', alpha=0.5, label='tick')\n",
    "\n",
    "plt.plot(tick_df.loc[s_date:e_date].price, marker='*', ls='', label='tick bar')\n",
    "\n",
    "plt.plot(vol_df.loc[s_date:e_date].price, marker='o', ls='', color='g', label='vol bar')\n",
    "\n",
    "plt.plot(dollar_df.loc[s_date:e_date].price, marker='^', ls='', color='y', label='dollar bar')\n",
    "plt.legend()\n",
    "plt.savefig('./images/tick_volume_dollar.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **(b)** Count the number of bars produced by tick, volume, and dollar bars on a weekly basis. Plot a time series of that bar count. What bar type produces the most stable weekly count? Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_tick_count = tick_df.price.resample('1W').count()\n",
    "weekly_vol_count = vol_df.price.resample('1W').count()\n",
    "weekly_dollar_count = dollar_df.price.resample('1W').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_mean</th>\n",
       "      <th>count_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>317.515</td>\n",
       "      <td>193.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>905.638</td>\n",
       "      <td>532.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>806.258</td>\n",
       "      <td>556.688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count_mean count_std\n",
       "tick      317.515   193.028\n",
       "vol       905.638   532.047\n",
       "dollar    806.258   556.688"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df = pd.DataFrame(columns=['count_mean','count_std'], index=['tick', 'vol', 'dollar'])\n",
    "count_df.loc['tick'] = [weekly_tick_count.mean(), weekly_tick_count.std()]\n",
    "count_df.loc['vol'] = [weekly_vol_count.mean(), weekly_vol_count.std()]\n",
    "count_df.loc['dollar'] = [weekly_dollar_count.mean(), weekly_dollar_count.std()]\n",
    "count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "plt.plot(weekly_tick_count)\n",
    "plt.plot(weekly_vol_count)\n",
    "plt.plot(weekly_dollar_count)\n",
    "ax.legend([\"tick\", \"vol\", 'dollar'])\n",
    "plt.savefig('./images/bar_count_series.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale(s):\n",
    "    return (s - s.min()) / (s.max() - s.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "plt.plot(scale(weekly_tick_count))\n",
    "plt.plot(scale(weekly_vol_count))\n",
    "plt.plot(scale(weekly_dollar_count))\n",
    "ax.legend([\"tick\", \"vol\", 'dollar'])\n",
    "plt.savefig('./images/scaled_bar_series.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(c)** Compute the serial correlation of returns for the three bar types. What bar method has the lowest serial correlation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log returns\n",
    "tick_returns = np.log(tick_df.price).diff().dropna()\n",
    "vol_returns = np.log(vol_df.price).diff().dropna()\n",
    "dollar_returns = np.log(dollar_df.price).diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>returns_autocorr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>0.116565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>-0.168050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>0.086624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        returns_autocorr\n",
       "tick            0.116565\n",
       "vol            -0.168050\n",
       "dollar          0.086624"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns_corr_df = pd.DataFrame(data={'returns_autocorr':[tick_returns.autocorr(),  \n",
    "                                                         vol_returns.autocorr(), \n",
    "                                                         dollar_returns.autocorr()]\n",
    "                                    },\n",
    "                               index=['tick', 'vol', 'dollar'])\n",
    "returns_corr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(-0.6,0.6,0.01)\n",
    "fig, ax = plt.subplots(3,1)\n",
    "ax[0].hist(tick_returns, bins=bins)\n",
    "ax[0].set_title('tick')\n",
    "ax[1].hist(vol_returns, bins=bins)\n",
    "ax[1].set_title('vol')\n",
    "ax[2].hist(dollar_returns, bins=bins)\n",
    "ax[2].set_title('dollar')\n",
    "[a.set_yscale('log') for a in ax]\n",
    "fig.tight_layout()\n",
    "plt.savefig('./images/serial_corr_bars.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the Autocorrelation functiono**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "fig, ax = plt.subplots(3,1)\n",
    "sm.graphics.tsa.plot_acf(tick_returns, lags=20, ax=ax[0], title='tick')\n",
    "sm.graphics.tsa.plot_acf(vol_returns, lags=20, ax=ax[1], title='vol')\n",
    "sm.graphics.tsa.plot_acf(dollar_returns, lags=20, ax=ax[2], title='dollar')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(d)** Partition the bar series into monthly subsets. Compute the variance of returns for every subset of every bar type. Compute the variance of those variances. What method exhibits the smallest variance of variances?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#monthly_ticks_returns_var = tick_returns.resample('M').var()\n",
    "monthly_tick_returns_var = tick_returns.resample('M').var()\n",
    "monthly_vol_returns_var = vol_returns.resample('M').var()\n",
    "monthly_dollar_retuns_var = dollar_returns.resample('M').var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>monthly_returns_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>6.799775e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>7.809432e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>6.030135e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        monthly_returns_var\n",
       "tick           6.799775e-11\n",
       "vol            7.809432e-11\n",
       "dollar         6.030135e-11"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_ret_var_df = pd.DataFrame(data={'monthly_returns_var' : [monthly_tick_returns_var.var(),  \n",
    "                                                                 monthly_vol_returns_var.var(),\n",
    "                                                                 monthly_dollar_retuns_var.var()]\n",
    "                                       },\n",
    "                                  index=['tick', 'vol', 'dollar'])\n",
    "monthly_ret_var_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(e)** Apply the Jarque-Bera normality test on returns from the three bar types. What method achieves the lowest test statistic?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import jarque_bera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "jb_value_tick, _ = jarque_bera(tick_returns)\n",
    "jb_value_vol, _ = jarque_bera(vol_returns)\n",
    "jb_value_dollar, _ = jarque_bera(dollar_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jarque_bera_results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>1.195019e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>3.873030e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>7.664635e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        jarque_bera_results\n",
       "tick           1.195019e+11\n",
       "vol            3.873030e+13\n",
       "dollar         7.664635e+12"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jb_test_df = pd.DataFrame(data={'jarque_bera_results' : [jb_value_tick,  \n",
    "                                                         jb_value_vol,\n",
    "                                                         jb_value_dollar]\n",
    "                               }, index=['tick', 'vol', 'dollar'])\n",
    "jb_test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the comparations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_mean</th>\n",
       "      <th>count_std</th>\n",
       "      <th>returns_autocorr</th>\n",
       "      <th>monthly_returns_var</th>\n",
       "      <th>jarque_bera_results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>317.515</td>\n",
       "      <td>193.028</td>\n",
       "      <td>0.116565</td>\n",
       "      <td>6.799775e-11</td>\n",
       "      <td>1.195019e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>905.638</td>\n",
       "      <td>532.047</td>\n",
       "      <td>-0.168050</td>\n",
       "      <td>7.809432e-11</td>\n",
       "      <td>3.873030e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>806.258</td>\n",
       "      <td>556.688</td>\n",
       "      <td>0.086624</td>\n",
       "      <td>6.030135e-11</td>\n",
       "      <td>7.664635e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count_mean count_std  returns_autocorr  monthly_returns_var  \\\n",
       "tick      317.515   193.028          0.116565         6.799775e-11   \n",
       "vol       905.638   532.047         -0.168050         7.809432e-11   \n",
       "dollar    806.258   556.688          0.086624         6.030135e-11   \n",
       "\n",
       "        jarque_bera_results  \n",
       "tick           1.195019e+11  \n",
       "vol            3.873030e+13  \n",
       "dollar         7.664635e+12  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([count_df, returns_corr_df, monthly_ret_var_df, jb_test_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Eq9hFNaiuheVQTCadXmm/ZuvQKlokJgLMhyI4e7toH6y8BLQWLQrLjpu1gmOr2EU1qK6F5VBMJpFIrHxSi9AqWpRPd2la5eQOtWhR3ODYpKnsW8UuqkF1LSyHYjKDg4Nmd0EZWkWLokNZYroLatOiuNKrSUcorWIX1aC6FpZDMRnV15XXk1bRIrJEUa1yatGilILlLM24UblV7KIaVNfCcigm43Q6ze6CMrSKFsWA/K1LO5RatGgf6MPZ1818KEJi/OoN9081WsUuqkF1LSyHYjJer9fsLihDK2iRnAySujZNm68T9/DQkufVooWmaU2dKLIV7KJaVNfCcigmMz09bXYXlKEVtCimrN+/e8mAPNSuRSGO0owrvVrBLqpFdS0sh2Iy3d2L8zi1Kq2gReEPvvdA5ZQrBWrVwp8P8DfjSq9WsItqUV0Ly6GYjOrLAOtJK2hR+IPvX2aFF9SuRTNXb2wFu6gW1bWwHIrJJJNJs7ugDK2gRWiJKo3ZnE4mmyt+X6sWHcObsHd2kAoESU3N3HhHFaIV7KJaVNfCcigmo/q68nrS7FqkZ8MkLwWwudvx7Nx6XdsHHz3PW798mljaqMhXqxaazVbM69Vs017Nbhe1oLoWlkMxGdXXldeTZteiGD+5eSea3V48Pp/TeWoiTDCe4blrMWB1WviadINjs9tFLaiuheVQTMblcpndBWVodi0KK7wWxk+uhFJk8/sRzwbjwOq0KK70arIRSrPbRS2oroXlUEzG7Xab3QVlaHYtikW1FqSsH5srzYuPTBkOZTVa+PP3bbYRSrPbRS2oroXlUExmdnbW7C4oQ7NrUcjh5V2QcqXcoZydNhzKarTw7NqGrd1J/MIl5iOxG+ipWjS7XdSC6lpYDsVkent7ze6CMjSzFvPRGLFzE2iONrx7tl/XNlHmUK5FM8wmMqvSwuZoo3PPDqC5RinNbBe1oroWlkMxmUgkYnYXlKGZtYicGgVdx7t3B7b26/Mxjc0aDqXDYfw6ng3GV62FL79hspkcSjPbRa2orkVbvd5ICPFG4F3AbUCHlHLZ9xZCvBD4G2A/cBX4L1LKz5e1bwA+AbwKSAKfBd4vpcxVuJ2yqF4wp540sxaFpbwLp7uyOZ2JkOFQXrK9m2+NTDMSTDDQszotSjXmz95Ab9Wime2iVlTXop4jlFkMB/HulU4UQviBbwJfBbqBdwKfEELcXXbaF/L/bgbuBH4OeO9adrgeqL6uvJ40sxZL1UAJRNJksjp9HQ5u39QJwNmp+Kq18DZh9cZmtotaUV2LujkUKeXDUsovAuerOP3ngTjwESllSkr5beD/AG8HEEJsB14JvFdKGZJSngc+jOF4qmZycrL4FY1Ga7l0zVB9XXk9aWYtlkpZP56Pn2ztdrG7vwMwprxWq4V3306w2YiOXCCXUvvTbLU0s13Uiupa1G3Kq0ZuA56WUpZXCzoGvLmsPSSlPLegfZsQwielDFfzJg899FDx9aFDhxBCEAgE8Hg82O12wuEw/f39zMzMoOs6/f39TE5O0tlpfJKMRqMMDAwwNTWFpmn09PQwNTWFz+cjm80Si8UYHBwkEAjgcDjw+/0Eg0H8fj/pdJpEIoHD4WBsbAyn04nX62V6epru7m4SiQTJZLJ4vcvlwu12Mzs7S29vL5FIhHQ6XWx3u904nU5CoRB9fX2EQiEymUyxvZ7PVGiv9ZlSqRRjY2NN9UyRSIRkJEp05AJoGsk+P3Nzc8VnOjF+DYDNXgeZ2QDuNo1gPMOV2Sj9/fFVPZNr2xDJ8xPMnDhDrN+/Ls9UT9vL5XIEg8F1/zk1wu+TpmmMjY2Z+kzLodW7wpsQ4mXAo8vFUIQQnwHapJRvLTv2a8AfSSl3CiHeDPyZlHK4rH07xuhni5Ty0kr9OHLkiD40VKpH4fF4qhJsrZmbm6Orq6vu76sizapF6JnneOI1v45n1zbu+/4/Xdf2ke9c5NHRWf7ToS389N4+3vt/z3L8apT33TPA4Zs3rer9jv/Wf+XqvzzCLf/f+9jyptevxSOYSrPaxWpQQYtjx45x+PDhirUXVF3lFQH8C451AeEV2gttVTEwMFD8MsOZAIRCIVPeV0WaVYti/KRChcbCHpThLmMH9K4+Y9rrdGD1WpQC880RR2lWu1gNqmuhqkM5Dty+4Ngd+eOFdr8QYseC9otSSrUVX0BfX5/ZXVCGZtWiGD9ZEJDP6ToTcykAtuYdyu68Q7mStLNamq16Y7PaxWpQXYt6Lhu2Aw7Amf++kJQmtSBWAkYA/iNCiPcCfwnchxGofxWAlPKCEOLR/DlvA3qB9wGfXPcHWWNCoRAej8fsbihBs2pRdCgLlgxPRTMk53N0u9vwuYxfxVJgPoGu68tWdVyKwtLk6Olz6NnsdYkoG5FmtYvVoLoW9RyhvBlIAA8D9vzrBDAshLhPCBEVQmwFkFLOAQ8AbwBCwN8B75RSPlF2vwfz/b8MPAV8HfhInZ5lzchkMmZ3QRmaUYtcZp7I6VGgNHIoMDZnFEsqjE4ANnqddDrthNM5puOr08PZ7cO1eZBsIkns3MQqe64OzWgXq0V1Leo2QpFSfg743BLNF4HrghhSyqeAFy9zv2sYo5aGRvV15fWkGbWIjY6RS6VxD2/C4fde1zae3yFf7lA0TWNXXwdPX4kwEozT57l+V321+A7sJnkpQPjkCJ27t626/yrQjHaxWlTXQtUYSsug+rryetKMWiw13QVlAfnu61OS7+4zMsoWMg+vhmJgPp8yv5FpRrtYLaprYTkUk1F5PrTeNKMWpRVei2vIFzc1dl3vUHbl4ygjwRtwKE0UmG9Gu1gtqmthORSTsTd4wHQtaUYtlkq5out6MSnkcNfCEcr1gfnVUBihRE6OrPoeqtCMdrFaVNeiaocihHi9EELVnfUNSzhc1ab+lqDZtNBzuWKSxoUB+Zn4PPFMDm+7nS739b9WA51OOh0aoeQ8U7HVBWHbN/bj6OkiMxcheUntaZKVaDa7uBFU16KWEcqfAFeFEH8thLhzvTrUavT395vdBWVoNi3iFy+TjcVpH+yjvb/nurbCCq/hLteipcFGYP7G4iiaphU3Ujb6tFez2cWNoLoWVTsUKeVtGAkZE8BXhRDPCyH+WAixbb061wrMzMyY3QVlaDYtCgHxhdNdUKqBsrW7co3wTflKrzcUR2mSVPbNZhc3gupa1DSFJaU8DhwXQvwBcBj4KPBBIcQPMDYVfrHR6pGYTaPPb68lzabFUvETKAXkF8ZPCmzzGXPla+NQGnulV7PZxY2guhY1B+WFEDcBDwF/C7jyr/8O+G3gn9e0dy2A6kPYetJsWhSmmgpVFMsZW2KFV4GD2wcAI5X9qgPzTbLSq9ns4kZQXYuqRyhCiHdh7HbfBXwZeLOU8kdl7V8Frq15D5ucyclJhoeHVz6xBWgmLXRdXzKHV/kKr6WmvLKRabpcbcwl5wlE0mz0tdfch47tm7F7OkhdnSIdnMXZ113zPVSgmeziRlFdi1pGKPdjTHFtklL+VrkzAZBSxmmCnev1xqwsxyrSTFokL0+SmQnh6PHjGhq4rm0uOU8klaXDYaOvw1Hxeq/Xe13BrdWg2Wx4b9kJNPYopZns4kZRXYtaHMp3pJRfkVKmyg8KIX6/8FpK+cia9czCooEpxU92L1rFVZ5yZbnkj4VU9muywbFJUtlbqE0tDuWhJY7/8Vp0pFUxq/SwijSTFsX9JxVSrowvkXKlnGg0WtzguCaB+QYeoTSTXdwoqmuxYgxFCPGKwrlCiJcD5R+pdlBDQSuLxQwMDKx8UovQTFpUs8JrqYA8GFp0Zo2VXmeDCXK6jm0VqeybYYTSTHZxo6iuRTUjlM/kv9qBz5Z9/2ngbcDvrFvvWoCpqSmzu6AMzaTFcjm8lkoKWc7U1BS9Hgc9HW3E0lmuhlNLnrscnbu3oznaiJ+fYD4aW9U9zKaZ7OJGUV2LFUcoUsrtAEKIf5BSvmX9u9RarKaAUrPSLFqkpmZIBYLYOzvo2Da0qL1S2vqFFLTY3dfBj8bDjATjDPmXPn8pbE4H3r07CJ8YIXJqlO47b6v5HmbTLHaxFqiuRS075S1nsg709PSsfFKL0CxalFLW70KzXf8rFk7OM5OYp73NxobOpWudFLQoTxS5WrwNXmO+WexiLVBdi2VHKEKI56SU+/KvJ4CKO6yklFvXoW8twdTUlNLryutJs2ixXPxkIj/dtcXfvmxMpKBFcaXXDdZGuUzjBuabxS7WAtW1WGnK6zfLXr9pPTvSqvh8PrO7oAzNosVqimotpKBFwaGcnY6vPjCfj+M0qkNpFrtYC1TXYlmHIqV8vOz1d9e/O61HNps1uwvK0CxaVBOQXy5+AiUtejoc9HkcBGMZLoVSK15XCe/NN4GmET1znlwqja19dWWFzaJZ7GItUF2LWuqh/L4Q4vb867uEEONCiAtCiLvXr3vNTyzWmCtv1oNm0CIzFyYxdgWby4ln1+KpiYkqRyjlWuy+wWmvNk8Hnpu2oM9niTx/YVX3MJNmsIu1QnUtatnY+HtAwRr/O/Ax4M+Av1jrTrUSg4ODZndBGZpBi/BJY0Ojd99ObG2LJwCWqtK4kHItdpdNe60Wb1kFx0ajGexirVBdi1ocil9KGRJCeIHbgL+SUn4GWDyut6iaQKCxq+mtJc2gRSnD8OL4SSydZSqWwWHXGPQun+yxXItiHOUGA/PQmCu9msEu1grVtailHsqEEOIe4Bbge1LKrBDCB1Q1qSeEsAMfAn4VI+39I8A7pJTBCud+APjAgsMeDCf2u/lzLgKDwHzZOXdLKU/U8Eym43BUTg7YijSDFuU5vBZSvsLLbls+uF6uRaF649npBNmcvuK1lWjkVPbNYBdrhepa1OJQ3otR7yQN/EL+2M8AP67y+j8EXg/cCUxj7Lr/R4wsxtchpfxz4M8L3wshdgNngM8vOPU3pJQLjzUUfr/f7C4oQzNoEX62csp6qC7lSoFyLbrcDgY6nUxG00yEkmzrdtfcr8IIJXJqFD2bRbPba76HWTSDXawVqmtRtUORUn4D2LTg8FfyX9XwduBPpJTnAfJVH0eFEMNSyrEqrn1aSlmt86qKycnJ4muPx2NKauhgMIjH46n7+6pIo2sxH0sQOzeOZrfTuXfHovaxKnbIF1ioxa6+DiajaUam4qtyKM7eLlxDAyQvTxI7P0Hnrm0138MsGt0u1hLVtaipBLAQwo8RM1n4l/c/VriuC9gKHC0ck1KeE0KEMeIxSzoUIUQ7xjTZwikwgI8JIf4nMA78rZTyk1U8RpGHHiolUD506BBCCAKBAB6PB7vdTjgcpr+/n5mZGXRdp7+/n8nJyaLjiUajDAwMMDU1haZp9PT0MDU1hc/nI5vNEovFGBwcJBAI4HA48Pv9BINB/H4/6XSaRCJBR0cHY2NjOJ1OvF4v09PTdHd3k0gkSCaTxetdLhdut5vZ2Vl6e3uJRCKk0+liu9vtxul0EgqF6OvrIxQKkclkiu31fKZCe63PlM1mGRsba9hnOv/Y45DL4d6znUuTgUU/pzNXZwHoc+YYGxtb9pmSySTxeLz4TDu6HDwOPHtphhf0ru6Z2nZshsuTXPrBT3A7tVX/nOptezabjWAwuK621yi/T06nk7GxMVOfaTm0asuLCiF+FfhfQBQojw7qUsrFH8euv3YLxh/9HVLKC2XHx4A/Wm7aSgjxIEa54U1SymjZ8ZdiOKgU8DLgS8AHqnUqR44c0YeGSnmWzBqhXLt2jQ0bNtT9fVWk0bUY++xXee4DH2Xolx7gwF8ururwli+fIhBJ83e/sJfhFUYZC7U4einM+791jps3ePiL1y2Oz1TD2f/xac599LNs/60H2fPQu1Z1DzNodLtYS1TQ4tixYxw+fLhiIK+WEcp/A35RSvnNVfShkOJ+4QRgFxBe4dp3AF8odyawaKPlt4UQH8PYzV/1KEWFVNCJxOpzNDUbja7FcilXkvM5JiNp7BpsqqKc70ItCiu9RqfjLReYb3S7WEtU16KWZcNtGCuzakZKOYcxQjlYOCaE2AH4gGeXuk4IcTNwH/CJKt4mx/W1WhoC1deV15NG1yKyzJLhS3NJdGDI78JhX/nXbqEWPlcbG71O0tlSPfp9rWsjAAAgAElEQVRaKS+2Ve3MhAo0ul2sJaprUcsI5cPAHwsh/lRKmVvFe30KeJ8Q4jGMVV4fBh6WUl5c5pp3AD+SUh4vPyiEGMYo7vUEkAEOYWy8/NNV9MtUAoGA0sne6kkja5FLpYmcOQ+aVqzjXk61KVcKVNJid18HVyNpRoJxdvTWHph3DQ3g6PGTmQmRvHIN95D5I/RqaGS7WGtU16IWh/J7GPs+/kAIMV3eUGW24Q8B3cBTGMW6vk0+4WQ+TvJJKWUxiCGEcANvAd5d4V4ejJ36OzEyII9jrCD76xqeRwmczsbKq7SeNLIWkecvoGfm8ezcSlvn4lU4hRooK6VcKVBJi139HXz3whwjwTiv2dNbcx81TcO3fzfT33uK8InnG8ahNLJdrDWqa1GLQ7mhbMNSyizwnvzXwrYvAF9YcCyB4YAq3es0cMeN9EcVvF6v2V1QhkbWojDd5a2QYRhqH6FU0qK4Y/4Ga8wbDmWEgde8ZNX3qSeNbBdrjepa1LIPxco2vA5MT0+bsrpMRRpZi/CzRkDeXyEgD+WbGlcOyENlLXb1uvGmMjieCxB6+TB+3yoyDx/YBTRWTq9Gtou1RnUtqnYo+f0gDwG/DPRKKf1CiFcDuxtxqkkVursrDsJakkbWIrRMyvp0NseVcAqbBpurLONbSQuP085ts1E64mkee3SUn/35/TX3sxSYP1vztWbRyHax1qiuRS2rvD4O7AcepFS58RTw/6x1p1oJ1ZcB1pNG1ULPZomcHgUqT3ldDqXI6TDobae9rbpfuUpaXJ2YoyOeBuDiqUlyudpXanl2bMHudpG8PEl6JlTz9WbQqHaxHqiuRS0O5eeAX5FSPoGxRBcp5WVgaNmrLJYlmVzdEtBmpFG1iI2Ok0ukcG0exNm9uKJeYbprpZT15VTS4ugPSgkl5hMZJs5PLzpnJTS7He9+Y9qrUfajNKpdrAeqa1GLQ0mzYIpMCNGPsQTYYpWovq68njSqFoUNjf4K011QlsOryhVesFiL8FyCkVOTaDaNS15jyfCpp6+sprulaa983Ed1GtUu1gPVtajFoXwF+N9CiO0AQoiNwF9jpDyxWCWq1zeoJ42qRWiZlPWwuhHKQi2e/tE4ek5n580DXOzKr/Y6NUk6PV/p8mVptB3zjWoX64HqWtTiUD4AnAdOYKRMOQtcBT64Dv1qGVyu2lfqNCuNqkXkRL5K40pLhmsYoZRrkU7N8+yPJwB48X3b6O3zMNvuIJPOMnrqWs39bbTqjY1qF+uB6lrUsg9lJ/A8Rp0SO/C1RitmpSJud+07npuVRtRCz+VKObwqTHnN53Quh1KAUVirWsq1OPX0FVLJeTZt7WLjli52n5vjea+L7lSG089c5uY7FlaVWB7vnu1obXZi5yaYj8Vp83TUdH29aUS7WC9U12LFEYoQQhNCfBZjZPIB4LXAbwJPCyH+XgjRcPmzVGJ2dtbsLihDI2qRGL/CfCRG+4ZeXAN9i9qvhFPM53QGOp24HdUXtSpooed0jv3wIgAH7zFSbuzu6yDgcYGmMTY6TTRcW6DW1u6kc88O0HUip8/VdK0ZNKJdrBeqa1HNlNfbMdLD3yWlHJZS3p1PtXI3RuLGd6xj/5qe3t7aU2g0K42oRaFG+1LTXeM1FNUqp6DFhbNBZoNxvH4Xu28xUqXs7utg3m4j6nej6/Dc8as197sYR2mAGvONaBfrhepaVONQ3gz8rpTyqfKD+e/fnW+3WCWRSGTlk1qERtSi8AfZd+sKAfka4idQ0uLoDy4CcMfdw9jyWYp39LqxaXC+3agvfvqZ2ld7FVd6nVB/pVcj2sV6oboW1TiUm4Gl0q58N99usUrS6bTZXVCGRtRiuRooUHsOrwLpdJrgZISx0WnaHHZufdHmYpvbYWdLl4vJjnYc7W1MXY0wdbW2PzSFEUojBOYb0S7WC9W1qMah2KWUFa01f7yWlWIWC1B9XXk9aTQtdF0v7uVYyqGsdoQyODjIsR8aGxn3HxzC5XZc1767rwNd0+jY2gXAqWcu13R/7y07QdOInDlPLp2p6dp602h2sZ6orkU1q7wcQoiXs3Txqprq0ltcj+r1DepJo2mRCgRJT8/h6PLi3rL4Fz2b05lY5Qhl7MIlTuc3Lh68Z3F1iN19HXz77AyhHg8Q5Mzxq7zkp/Zgq7KSY1unh47tm4mfnyA6cqE4BaYijWYX64nqWlTjDK4Bn12h3WKVqL4MsJ40mhaF6S7v/t1o2uI/5JPRNOmsTl+HA4+z+hVeAOOjUebnc2zf009P/+Lssrv7jaW+IxmdF/d2MDcdZ/zcNNt2LV5pthS+/buJn58gfGJEaYfSaHaxnqiuxYoORUq5rQ79aFlUL5hTTxpNi5Wmu1aTcgUgO59j5HgQgBfeW/nT6I4eIzA/Hkrx4IFBnvrOeU4/c6U2h3JgN4F/PaL8jvlGs4v1RHUtrPiHyYRCjZHxtR40mhbhZWrIw+pSrgA8fzJAPJahd0MnW2+qvEy0vc3Gtm4XOR3cw0ZK81pTsTTK0uFGs4v1RHUtLIdiMn191X+ibHYaTYvikuElHEphhdeWGhyKruvFpcIvuHe44lRagUIFx8sZnU1bu2pOxVKY5oqcGkXP5aq+rt40ml2sJ6prYTkUk1H9E0c9aSQt0sFZkpcnsXe48ezYUvGciVWs8LoyPsfk5TDtLjv7bl8+pcruspLAhfQrp2tY7eXs66Z9Yz/ZWJz4hUtVX1dvGsku1hvVtbAcislkMmov2awnjaRFuFhDfheafXHAXdf1Ygyllimvnzx+EYAd+7pwrJCqpRiYDybYc2AQu732VCylDY7qTns1kl2sN6prYTkUk1F9XXk9aSQtitNdS6yOmoplSM7n6HK14XNVt7I+NBtn9PQkNrvGXS+vHOgvZ3u3G7tmjIT0Njs79myoORVLqSSwug6lkexivVFdC8uhmIzq9Q3qSSNpsWL8ZLb26a6nnxhH12HvgY1EYysnAXS22dje40YHzs0kStNeNRTeKqSMUdmhNJJdrDeqa1G3TYlCCDvwIeBXARfwCPAOKWWwwrkvAx4DYmWHn5VS3lN2zk7gExhJKmeBj0spP7pe/V8vPB6P2V1QhkbSYrmU9VB7ypV0ap4TPzHiGAfvHaatvbqpjV19HYxOJxiZivP6ff243A6mAkYqlv6N3hWvL1VvHEHX9WUXAZhFI9nFeqO6FvUcofwh8HrgTqCQmOgflzk/K6XsLPsqdyZ24N+A54B+4HXA+4QQv7Q+XV8/7BXm31uVRtFiPhIjfuESmtNB5+7tFc8Zr3GEcvLYZVLJeYaGuxkc8letRSmOEqetzcaeW40pkWpTsbg2D+Lo8pKZmSN1daqqa+pNo9hFPVBdi3o6lLcDH5ZSnpdShoA/AF4jhFhNHoGXAMPA+6WUcSnlMeCTwDtrucnk5GTxKxqNrqIbN044HDblfVWkUbQoBuT33oTNUXmQP17DCMWoeWLk7XpBfiNjtVqUr/QCuCU/7XXm+FVyOX3F6zVNK6beV3Xaq1Hsoh6orkVdpryEEF3AVuBo4ZiU8pwQIgzcBoxVuMwuhJgAHPnrPiClPJ5vuw0YkVKWe4FjwLtq6ddDDz1UfH3o0CGEEAQCATweD3a7nXA4TH9/PzMzM+i6Tn9/P5OTk3R2GqkwotEoAwMDTE1NoWkaPT09TE1N4fP5yGazxGIxBgcHCQQCOBwO/H4/wWAQv99POp0mkUjg9/sZGxvD6XTi9XqZnp6mu7ubRCJBMpksXu9yuXC73czOztLb20skEiGdThfb3W43TqeTUChEX18foVCITCZTbK/nMxXaa30mgLGxMeWfKfwTo1Cpe+92JicnFz2Ty+XiwozxB749FWJsbGbZZxobnWFuOk5Hp4OuDXZmZ2fJZDLE4/EVn6ktFqRNg0uhFM+NXmCgx4+3q53IXIpzZwI4PekVn8m+fQgeP8r493+M665blbO99vZ2gsHgutpeo/w+eTwexsbGTH2m5dB0feVPMTeKEGILMA7skFJeKDs+BvyRlPLzC84fBAaAU0An8D6MEc4BKeUVIcR/Bl4ppXxp2TUvB74tpazKSR45ckQfGhoqfu/xeKoSbK25dOkSmzdvXvnEFqBRtHj2t/+EK//8LW7+8HvZ+tafW9Q+Hc/wy/90Em+7nX9+04EV4xLy0z9m/PwML71/Dy+6z5hCq0WL3/7a84wE43zkgZ3cvsnLD4+M8sMjo9x8xyYeeMOtK15/5asP8+y7PsiG+1/Cwb//UFXvWU8axS7qgQpaHDt2jMOHD1c06noF5Qvp7/0LjncBi8ZwUsoAUFjOMAe8Xwjxi8D9wGfy96vqXssxMDBQy+nrQj0ceqPQKFqsmHKlrErjSs5k6mqE8fMzOJzX1zypRYvdfR2MBOOcDca5fZOXm2/fxA+PjBqpWF4/j9O5/K95IReZqntRGsUu6oHqWtQlhiKlnMMYoRwsHBNC7AB8wLNV3iZHKYX+cWC3EKJ8ycMd+eMNRX9/v9ldUIZG0CIbTxIduYhmt+Pdt7PiObWs8Dqarxe//wVDtLtKNU9q0WJXWWAeoKu3o6ZULJ6btmB3u0heCpCeUW8ndiPYRb1QXYt61jL5FMZKrMeAaeDDwMNSyosLTxRCvALDAZ0HOoD3YEyBPZw/5XsYcZc/F0L8IbAHo7b9f1rnZ1hzJicnla5vUE8aQYvImXOQy+HZuwO7u73iOdWu8IpFU8YmRA0O3n39c9eixe4+I6V5ITAPcPMdm7gyPsfpZy4X96cshWa303nzTYSOniJy6iy9972wqvetF41gF/VCdS3qucrrQxhLfZ8CLgN24E0AQogHhRDlAfbbgCMYU1vngbuAV0kpJwCklFngtcB+DOf0DeB/SCm/VJ9HWTvMiNuoSiNosdIOeah+hPLsjyfIzue4aU8/3X3X7y+oRYvhbjdOu8aVcJpIysg2XGsqFpVTsDSCXdQL1bWo2wgl7wTek/9a2PYF4Atl338c+PgK9xsFDq9xNy0slmWlDY1QXdnf+fkczzw5AcAL7t12Q31qs2ns6HFzZsqIoxwc8uHucLJj7wbOnprkueNXi8H+pSimsld06bBFY2ClXjEZs/a/qEgjaBF+dvmA/FwiQyg5T4fDRl+Ho+I5AM+fuEoskqJ/0MuWHT2L2mvVorDB8cy166e9oLpULCqPUBrBLuqF6lpYDsVkVFhppgqqa5HLzBsxFJae8hqfSwFGDZSlVngZNU+MrVcHl6h5UqsWt28y0qw8dm62uBJox+7rU7EsR+feHWhtdmLnxpmPJWp67/VGdbuoJ6prYTkUk5maUjPdhRmorkV05AJ6OkPH9s20eSvnVKqmSuOli7NcuxLG7XGy79aNFc+pVYu7tvrpdrcxNpfk9KSRAs9eQyoWu6vdSCOTyxF5brSm915vVLeLeqK6FpZDMRkVk/GZheparFRDHqqrI38sPzq5/c4ttC1R86RWLdpsGq/eZUydfeP56eLxWlKxFFKwRBSb9lLdLuqJ6lpYDsVkenoWz5+3KqprsVLKeoDxOWO6aKkRytxMnLPPGTVPbr9z65L3WY0Wr9ljlIf93vlZovnVXhu3dNHV20E0nGL83PRyl+M7sAtQLzCvul3UE9W1sByKyag+hK0nqmux0g55KFsyvMQI5eknxkCHvbduxOOtvI8FVqfFkL+d2zd1ksrq/Mc5o56KpmncfHuhPPDywflSYP5sze+9nqhuF/VEdS0sh2IyPp/P7C4og8pa6NkskZPGH9qlAvKR1Dwz8Xna7RoDnc5F7alkqebJC+5ZfnPaarW4Pz9K+caZ6WJwvrDaa+TkJOn8yKXiexamvM6cI5dZ+rx6o7Jd1BvVtbAcislks1mzu6AMKmsROz9BNp7ANTSAs6+74jmFgPyWLhe2CnPdp45dIp3KsnlbNwNDC1PRXc9qtbh3mx9fu53zM4lSKpaeDoaGu5jPZDl7enLJa9u8Hjq2DaGnM8TOXlzV+68HKttFvVFdC8uhmEwsFlv5pBZBZS2K0137dy15znIpV3I5naOFmieHtq34fqvVwmm38apCcP5MKWZSnPZaYU9KYcFBKL8AQQVUtot6o7oWlkMxmcHBQbO7oAwqa1Ha0LjyDvlKKVfOn7lGaCaBv9vNTXs3rPh+N6JFYdrrsXOzxNPGJ9o9t27EbtcYP7d8KhZvPj4UUSgwr7Jd1BvVtbAciskEAoGVT2oRVNaimpQry+XwKm5kvGcYm23lpZ83osXWbhf7Bz0k53N857wRnHe5HezYuwFdx0hIuQQ+Bas3qmwX9UZ1LSyHYjIOx9LpOVoNVbXQdb1syXDtObyuXQkzcWEGZ7ud/S+orjjSjWrxQFlwvkA1qVhKOb3OoudyN9SHtUJVuzAD1bWwHIrJ+P3LB2dbCVW1SEwEmA9FcPZ20T7YV/GceDrLtWgGh01j44LlwIXYyf4XbKbdVV0+1hvV4r7tXXQ67YwE45ybNoLz1aRiae/voX2gj2w0Tnxs5Rxg9UBVuzAD1bWwHIrJBINBs7ugDKpqUT7dtdRO5YmQMTrZ7G/HXjalFYukOHP8SsWaJ8txo1q0t9k4vPP64Hy1qVgKCw9U2TGvql2YgepaWA7FZFT/xFFPVNWi6FBWkXLl+I8nyGZ1du7bQFdvR9XvuRZaPLC3F4AjozMkMkZw/pY7hgB47pmlU7EU4kShE2qs9FLVLsxAdS0sh2Iy6XTa7C4og6paRKooqlUpKeR8JsszPxoH4AX3bKvpPddCi+09bvb2dxDP5Pj+hTkANm7x093bQSyydCqWYk4vRQLzqtqFGaiuheVQTCaRUCtVuJmoqkUxIH/rMilXKoxQzjx7lXgszYaNXjZvr7wZcinWSosH9l4fnNc0jX0r7Ekpr41S2G1vJqrahRmoroXlUExG9XXl9URFLZKTQVLXpmnzdeIeHlryvEIMpTBC0fXSRsaD926rOUvsWmnx0h1ddDhsnL4W4+Ks8ceomIrlVOVULO6tG2nze0kHZ0lNmj9nr6JdmIXqWlgOxWRUX1deT1TUopiyfv/uJZ1Caj7H1XAamwabfMYKr4kLM0xdjdDhcbJ3iZony7FWWrgddl5+kzE6+mZ+lLJSKhZN0/Ddks88rEBgXkW7MAvVtbAcisk4nYuTCLYqKmpRiCN4DyyTcmUuiQ4M+dpx2I1fqWLNk7u20tZW+6/ZWmpRmPZ6dHSG9Lyxt2SlVCyF51UhjqKiXZiF6lpYDsVkvF6v2V1QBhW1KHxC9y+zwuufT1wDYM8Go4rj3HSc0TPXsNs1brtzy6redy212NXXwc5eN5FUlu9fNILzK6ViKTyvCiMUFe3CLFTXwnIoJjM9vXzRo1ZCRS1CK1RpPHopzGPnZmm3a7z5oDG/fSxf82Tf7ZvwdC5d82Q51lqLwiilMO21UioWb1lg3mxUtAuzUF2L6rbtrgFCCDvwIeBXARfwCPAOKeWiqJ8Q4gHgPcCtgB04CXxASvn9snN0IAGU54cYklKG1usZ1oPu7tpW/zQzqmmRng2TvBTA5m7Hs3NxdcX0fI6/+qFR3+RX7hhko7edVDJTrHlycIWaJ8ux1lq8/KZuPvnkZZ4NRLkUSrLZ7+LmOzZx9tQkp5++wovu237d+Z6dW7G5nCQmrpKZC+PoMq8Oh2p2YSaqa1HPEcofAq8H7gQKCY3+cYlzu4G/AnYC/cA/Ad8UQiycP3i1lLKz7KuhnAmovwywnqimRTF+cvNONPvi2u9ffnaSK+EUW7tc/OIBI4PwiZ9cJpPOsmVHDxs2rv6P8Fpr4XHaedmOLqC0hLg8Fcu1q+Hrzre1teHdtxMw8nqZiWp2YSaqa1FPh/J24MNSyvP5P/x/ALxGCLHoY5yU8gtSyv8jpZyTUs5LKf8WiAIvWssOTU5OFr+i0eha3rpqksmlU4m3GqppUVjhVSl+cjmU5EvPGCukfvfeLTjsNnI53ZjuAl5w77Ybeu/10KIw7fXtszNksjnsbbbiCrRKwflSokhzp71UswszUV2Lukx5CSG6gK3A0cIxKeU5IUQYuA0YW+H6A0AfcGJB01eEEA7gHIaz+pda+vXQQw8VXx86dAghBIFAAI/Hg91uJxwO09/fz8zMDLqu09/fz+TkJJ2dnQBEo1EGBgaYmppC0zR6enqYmprC5/ORzWaJxWIMDg4SCARwOBz4/X6CwSB+v590Ok0ikaCnp4exsTGcTider5fp6Wm6u7tJJBIkk8ni9S6XC7fbzezsLL29vUQiEdLpdLHd7XbjdDoJhUL09fURCoXIZDLF9no+U6G91mey2WyMjY0p80zBoycBSG/qJZVKFZ+ps7OTjxy5SCanc2izG396mlTKwdEnRgjPJvB1uWhzxYlGo6v+OWWzWeLx+Jo+kzs+xVa/g/FQhn89eo6fuW0rPRuNz5Snn7nCtn0uenp7ij8n915jGuzaU8/S+ys/bZrtud1ugsHgutpeI/0+jY2NmfpMy6HVYydsfqpqHNghpbxQdnwM+CMp5eeXuXYD8DjwL1LKPyw7fhj4Qf7b1wOfA35OSvmtavp05MgRfWiotFHN4/FUJdhaMzY2xvDw6ufamwnVtPj+oTcSGx3n7kf+Hn9ZHZTHzs3y3x+7iLfdzmd+cR9dbiOl+Jc+9SSXLs7yip/Zd0PxE1g/Lb52aoq/eeISd2zy8uEHdqLrOp/92PeZnY7zi7/2QrbtKmVTDj19mifu/w06d2/n0Pe+sOZ9qRbV7MJMVNDi2LFjHD58uOKmrHoF5Qu5shdmNusCwiyBEGIT8G2MAP77y9uklEfKvv2yEOKVwINAVQ4FYGBgoNpT1w2Xa3ExplZFJS3mozFi5ybQHG1495QC1tHUPJ/8kRF0/40XbSo6k8nLIS5dnMXZ3sb+Fyy9o75a1kuLwzu7+fSPL/P0lQhXwyk2+trZd/smfnhklNNPX7nOoXTuuwnNbic6OkY2nsTeYc7PRyW7MBvVtahLDEVKOYcxQjlYOCaE2AH4gGcrXSOE2AZ8H/imlPK3pZQrDaVyQG35LRTA7Xab3QVlUEmLyKlR0HW8e3dgay9tJvvc0avMJOa5eYOHn9rTWzxeSLNy4EWbcbbf+Oe09dLC297GS7YbwflvPW8E55dKxWJ3tePZNQy5HJEz59alP9Wgkl2Yjepa1G3ZMPAp4H1CiMeAaeDDwMNSyosLTxRC7AUeBT4npfzjCu37gQ7gGUAHfhp4M/DGdev9OjE7O4vPZ96STJVQSYvC/gtvWYbhkak4/3Y6iE0zAvG2fCqWaDjJmWevomlwx12LlxevhvXU4v69fTw6OsvDI9O8+QUbi6lYLo/Ncfb0ZDHFPRgpZ6JnzhM+MULXwVvWpT8roZJdmI3qWtRzldeHgH8DngIuY+wveROAEOJBIUT5Mqv3AUPAu4UQ0bKvB/Pt/cDfA7PANeCPgbdJKf+1Po+ydvT29q58UougkhYLa6Bkczp/8fg4OvDz+zewo7f0SfGZJyfIZXV23jxAV0/1NU+WYz212D/gYYu/nZnEPE+OGyvtl0rFosJKL5XswmxU16JuIxQpZRZjs+J7KrR9AfhC2fe/BvzaMvd6DDDn49IaE4lETFkMoCIqabEwZf2/PRdkdDpBv8dR3BEPRs2T408Wap6sXbB0PbXQNI379/bxqScv883np7l3Wxd7bt3If/z7c4yfmyYSSuL1G3P1xdooJu6YV8kuzEZ1LazUKyajesGceqKKFtlkiujIBdA0vPt2Mh3L8LmfGJ/cf+vuzbgdpU2Ozx2/SiKeYWDIx9C2tdvFvN5avGpXDw6bxlMTYa5F00umYimWA37uHLnM4lT39UAVu1AB1bWwHIrJqF7foJ6ookX0zHn0+SyencO0edx84slLxDM57trq457h0kJFo+bJRcCoyFhrzZPlWG8t/K427t3mR6cUnL8lH5w/XVZv3uH34h7eRC6VJja67HaxdUMVu1AB1bWwHIrJqF7foJ6ookUxfnLrbn5yKcx3z8/R3mbjXXdvuc5pTJyfIRiI4vG2s+fA2v6i10OL+/M75781Mk02p7N9dz/uDgfBQPS6VCw+kxNFqmIXKqC6FpZDMRnVlwHWE1W0KPzh7LhlN3/9wwkA3nzHIAPe62tRHP3BRQBuv3Mr9lXUPFmOemhx28ZONvmcBGMZfnIpjL3Nxp4Di1OxmB2YV8UuVEB1LSyHYjKqF8ypJ6poUXAoP3H1cSWcZrjbxc/nkz+CMdV15vhVzj0/hb3Nxm0vXl3Nk+WohxY2TeP+Pfma8wv2pDx3/Cq5nLH1y+wRiip2oQKqa2E5FJMJhRouQfK6oYIWucw8kdOjAMiEsZrmd+/dQpvNmOqaCkSQn36Kf//ycdDhthdvoaNz7X/J66XFq3f1YNfgyfEQ07EMG7f46e7tIBZJMX7OcDLe/AglcnIEPZdb7nbrggp2oQqqa2E5FJPp6+tb+aQWQQUtYqNj5FJpEv39xNrd/NTuHg4MdpKIpznyr6f5h7/6ARMXZnB3OHj1z93Cyx7Yuy79qJcW3R0O7h72k9Ph4ZFpNE1j34I9Ka6BPto39DIfiZEYr1wyeD1RwS5UQXUtLIdiMqp/4qgnKmhRmNaZ2DCEt93O2164ieNPjvPZj32fp380DprGwbuH+fX/9yXc+qIt2Gzrk+2nnlo8UBacz+l6xVQsZlZwVMEuVEF1LSyHYjKZTMbsLiiDCloEn3kOgGsbt/Ar23z822ef4ttfP00inmHLjh7e8tv38IrX7sOVTwq5XtRTi4NDXgY6nQQiaZ6+HMmnYulmPpPl7Cmj5ovvgLEfxYzAvAp2oQqqa2E5FJNRfV15PVFBi3NPnCTT4cW7Yz8XvnmGa1fCeLtcvPaXb0f8+ovoH/TWpR/11MKmacVEl187NUU2VxqlnH7GmOIqBebrX71RBbtQBdW1sByKyai+rryemK3FyUshwq4NnP2Fd2Gfd2eDD5oAAA/gSURBVNDWZuPuV9zE2959H3sODK7pxsWVqLcWr9lt7Jx/ciLM73z9eWwbvdjtGmP5VCy+ssB8vTHbLlRCdS0sh2IyHo/H7C4og5lanD09yb9/6kcE73gZOYeTXbcM8Gu/d4h7X7kLh3NxPfn1pt5a9HmcfPDVOxjodDI6neA9D19A7/dCPhWLe+sm2rweUtemSU4G69o363ekhOpaWA7FZOz2+v+xUhUztJgJxvjq/z7K1z//NG3zOu2zUxyYPMbrH7wDf/faZA5eDWZo8cLNPj71C3t5w4ENaBoc1Y0R2VNPGosRzEoUaf2OlFBdC8uhmEw4vGTBypajnlqkU/N891vP87m/fJwLz08xb9NIT11g59c+yY49G1a+wTpjll24HXZ+884h/uZn99I33E3appGYTfBfvvoc9j03GX2r87SX9TtSQnUtLIdiMv39/WZ3QRnqoYWe0zn19GU+87Hv89T3LpDL6gS7O3h8Sx87LjyNpueKK5rMxGy72NHr5uOv38OGncaS4plzQb4UM0ZsoTqPUMzWQiVU18JyKCYzMzNjdheUYb21CFwO8cVPPck3v3KCWCRFyOXgR5t6ONbtZVOvm97LRl2TQlEtM1HBLmyaxgOvMEYlw4k0VzZsBuD8k6c4NRld7tI1RQUtVEF1LepZAtiiArqum90FZVgvLeLRNN97ZISTP7kEQMpuY6Snk0Cnixdv9fOzt/SzT4/xvdkwjh4/rqGBdelHLahiF4VULLPTcd76wC3Mf7KNzuAU7/vKs7zy9s38+os24W1f3z8jqmihAqprYTkUk1F9CFtP1lqLbDbHE49f5Mn/GEXP5MgB4/4OAht8vHJfH6+7uZ9NvnYAJr/5NGDst6jn8uClUMUuNE3j5js28YNHR5m/Eqbrlp1Ejp9hY+AS3zjj5ocXQ7zjriFecVP3uummihYqoLoWlkMxmcnJSYaH1650bCOzllr84OhlfvjwCFo0BUDQ7SS8vY/7D27k8M5u2iJR4qOjXLlwifjFy0z9xxNAKVW72ahkF/tuNxzKmWcDeO/4GWy+PTzgtvFjJ5yOJPnwYxd5ZGSG3713M0P50sFriUpamI3qWlgOxWRUrg9db25Ui0w2x5ETk/z40VE6ZmJoQJocHua4Pz6F/3tTxP/hMj+8eJn5cOUYQM/dd9xQH9YKleyiq6eDfbdt5LnjVwnbOmDXbczFoPfMJPcBWU0jdnmGjx+/xI6tXezb3s2ebd0MDnRis994mFYlLcxGdS0sh2LRsOTm54lfCnDl9EWe+clZxq9koGuIDpsdLZOm//jj9J16Als2SwJIlF1r7+zAs30z7uEhOrZvpmPbEN59O/Hfsc+sx1Gan/6l23jFa/cx9vhJjv23zzK/aQv6rj1EdSeJrA1feh5fep74qQBHTwU4CuQ00DucuLrc9G7oZOtmH7uGu9mwoXPNC5JZqIHlUEwmGo3S29trdjeUoJIWsUicy2fGuPb8OHPnLxObmCR7bRpmo9gTSfQ2B/PuTqZvuRN6jGqD/nMnGHzqUTxuO+7b9xYdh2f7ZtzbhvBs24yjt0uJWMlSqGgX7g4nuw7dzMTEc2SfPwaPwQCQdbST6uoj1dVPsqufVHcfye4NzHv8EEuTiaUJXA4RePoyPwZ0dLQ2cHnb6Rn0suWmfnZt66Gv30ObY/HGPRW1MAvVtdDqtWpACGEHPgT8KuACHgHeIaWsmMdBCPEa4KPADuAc8PtSykfK2ncCnwDuBmaBj0spP1ptf44cOaIfPHhwdQ+zhiSTSVyutZ93Vg09p5NKzRONpYnE0sTjGWLxDPF4huhslNDVGWJTc2QiCbKJNGSygAb2NrIOFzmnC32FXcIdWpp79rnZfutWOrYN4fCpPT2wHCrbRfjkCLM/PkF6epZ0cJb09Fz+X+N1ZsZIsZ5tc+YdjeFsDIfTR8bbDZWcuZ6jLRnDmU3g1OZpd9lw+9tp97vxbOjFvaGbzoFuOvu66Wi343HacTvs2NephICKqGAXx44d4/DhwxVFr6dD+SPgrcBrgGngs0CHlPL+CufuAE4Cbwck8AbgU8AtUsqLeed0EngUeB+wF/gW8DtSyi9X058jR47oU6eXWEtfQRK9cHAFuZbVc0GbDsRiMTo6PMZ1xn/FL9DJ5b/R9WIP0PX8V+E8Xa/cnj+m54x2cjq5Bf8WziN/jq4DuVzxPUFHzxXeo+wZ9dKzFu6Ryxmvc5oNHRtodnSbHTQ72Ow3PiLQc9g0nTaHjfYOJx5/B51eFx0dDjZu6eKWOzatyZy9CkxMTLBly9qXFq4Hufl5MrPhkpMJzpIOzpGeniUxNcvM5Cxz4XliWTupNjcZTxeprj7S3h6wVfnz0/W8neYo/baAlv+t0PLHit9rlP1rbMDT8m9l0wz/pmnGMZumGedplGzWpqGh5XfuFdptpXPyTk0rnmfcUNNAg6ID1cruWbi1cS8N45/CQeM6jcL/jBeJRJyOjo7iPcua0Ar94vr7Fm5x3TX58ym+XdlrraxvZW2Fd+vd5VrSodRzyuvtwJ9IKc8DCCH+ABgVQgxLKccWnPtW4KiU8vP5778ghHhn/vgHgZcAw8D7pZRx4JgQ4pPAO4GqHArAidPxG3qgtaENSNXhfYomVddblZ9my6SwpZLYMyns6ST2VNI4Np9Gb7OD20l7l5eOfj/+zb0M7NjE4E0b6fC6aXe1VZwOaVZUno5bCVtbG+39PbT391R1fi6dIRSY4eL5AGOj1wgHo6QiadJpyOht5Iw//4CGbtOMDyk2O9jtQMkmyhXTF/y7LKVPcJCtqstVoi/x+kZoA9JrdK/V8YpdS4+Q6uJQhBBdwFbgaOGYlPKcECIM3AYsdCi3lZ+b51j+eKF9REoZXdD+rlr61Ra8XHytaRo2Tct/yilQ/l2VBqHrJfdeeF34aK8tf0utvN0YeqBpWn4koOVvpef/2OSHKIX7axoaev5loV3P30ov+gBd17HZjHtq6GiaDV3PoWml+9vtNnK5nDHjZDNeG5/8jXva29rIZrPYbGCz28nlsjja7Pn3yOFxtaFnkrTbNTyuNnKZBP5ON5qeJZvL4vP7ieWceLbfhH/3NmIOG339/UxPGzXMBwcHCQQCuN1uHE4IzgTo6+vj2lSITCZTbPd4PNjtdsLhMP39/czMzKDrOv39/UxOThZXxESjUQYGBpiamkLTNHp6epiamsLn85HNZonFYsV7OhwO/H4/wWAQv99POp0mkUgU251OJ16vl+npabq7u0kkEiSTyWK7y+XC7XYzOztLb28vkUiEdDp93TM5nU5CoRB9fX2EQpWfKZlMEo/Hm+qZlv05edro2d7Fvrv2LHqm9vZ22trais90ZXwCInHaUzmunR9DS8wTuTb7/7d3b6Fx1FEcx7+726okGhN8MEhrVbw8VFBRkILa0oIitIq3A6JoBbEi4gUvKZriDbWxKOiDYCul6IN6EHwoCmqhVCw+VCP6pOClKwqCRmuaDVWzWR9mdmeSbtJWJvOfZH+fl+3MfzY9c3Znzvx3Ln/G/zzIouNOYGz8EJTKlMsVDv0zQaWyiInJSSbqDSqVxfw9MUGJMo1ymfpEg1KlTH0SJhtQqlSo1xut3kdjMtlGGg0ol0tMTsYbaau91Oqtp7dXSLbXZn+J1uaanhe/p1Sa8rdS3Yd4H1KasldqLZXaxST7mPR+J710qj29i5q2W5r6jin/GzDzs+7y6qE0RyWaPn7lAaBnhuXbLbv8CO3t/taMbn5mbevf3d3dQS7Jq1arhb6uPA/NW7VGRkZauUjnpLe3F5j66O50e19fHwBdXV1t25snMdM/IaXbm+N0p+e1e0x4ur35Xenp6Wnb3pyf/k4dyzqNjo7S1dW1oNbp/35O1WqV/v7+1jqdec7ZrfbTVlx42DotZEXYXwwPD8/YlldBORi/njxtfi/Q7vGZB4+w7JHaj8qpp4Z/xEZ64+10ykVCuUgoF4mi5yKXM5jufgD4CWhdVhWfeO8Bvm7zlq/Sy8Yuiuc32881s+4Z2ueNej3TH23nNeUioVwklItE0XOR50n5rcCAme0musprCPjQ3fe3WfYN4BEzuxl4F7gRuBi4LW7/hOi8y3NmthE4D9gA3D+nazAHarVaqyvf6ZSLhHKRUC4SRc9FntdYbgZ2AvuAX4guz7gVwMxuMbPWCXZ3/x64Hhgk+hlrELiuWXzcvQ6sA84nKk4fAFvc/e28ViYr/f39oUMoDOUioVwklItE0XOR230oRVOUGxuLcJKtKJSLhHKRUC4SRcjFbDc2Loy7wOapsbEx9u7dy9hYfoMVFZVykVAuEspFYj7kQgUloFqtxp49e6jVaqFDCU65SCgXCeUiMR9yoYIiIiKZ6OhzKKFjEBGZj4I/HFJERBY2/eQlIiKZUEEREZFMqKCIiEgmVFBERCQTKigiIpIJFRQREcmECoqIiGRCBUVERDKhgiIiIpnIc4AtOQrxKJRfA8vcveM+HzM7HngFWA30A38C7wCb3P1QyNjmmplViMYNWg+cAHwEbHD330PGFYKZDQFrgaXAGPA+MODufwQNLCAzKwOfAiuApe7+c+CQDqMeSvFsBn4MHURAi4DfiQZQ6wUuJyouL4QMKicbgWuBS4El8bw3w4UTVJ1oAL5TgAuI8rEjZEAF8CAwHjqI2XTcEXCRmdkVRDvQR4BVYaMJw91rwOOpWVUz2wbcEyikPN0FPO3uPwCY2aPAd2a2zN2rYUPLl7s/lpr8zcxeBjxUPKGZ2blE28ANwJeBw5mReigFYWZdwDbgTuDfwOEUzRrgq9BBzCUz6wVOB75ozouHwh4lOkLvdAv+OzCT+Keu7cDDwIHA4cxKPZQ5ZmY7gNtnWeRZdx8Engd2uvvnZrYqj9jydgy5SL/nAWAlcMkchlYEJ8Wvf02bfwDoyTmWQjGzG4C7ib4Hneh+4Fd3f8/MzggdzGxUUObevURHFjMZN7PLgKuBC/MJKZgj5iI9YWYPAgPAanf/aS4DK4CD8evJ0+b3EvVSOpKZ3QS8Blzj7sOh48mbmZ0NPMQ8OaDSeCgFYGZPEu04m2N7LiY6Kh0B7nD3nYFCC8bMNgEbgDXu/m3oePJgZlXgKXffHk+fBXwPnOnu+0PGFoKZ3QG8CKxz972h4wnBzNYTFdTmAUcZ6CO6+nHQ3V8NFFpb6qEUw0vA66npFcBbRD2WkSARBWRmWwADVsbnETrFVmDAzHYTfe5DwIcdWkzuA54ArnL3faHjCciBXanpJcBnwJXAN0EimoV6KAUUn0PZ1aH3oSwD9gP/MPXihKq7Lw8SVE7i+1CGiO5DOR74GLirQ+9DaQATwN/p+e5+YpiIiiE+h/IjBb0PRQVFREQyocuGRUQkEyooIiKSCRUUERHJhAqKiIhkQgVFREQyoYIiIiKZUEEREZFMqKCIiEgm/gMyqD0gODU60QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tick_standard = (tick_returns - tick_returns.mean()) / tick_returns.std()\n",
    "volume_standard = (vol_returns - vol_returns.mean()) / vol_returns.std()\n",
    "dollar_standard = (dollar_returns - dollar_returns.mean()) / dollar_returns.std()\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "tick_standard.plot.density(ax=ax,\n",
    "                           xlim=(-5,5))\n",
    "volume_standard.plot.density(ax=ax,\n",
    "                             xlim=(-5,5))\n",
    "dollar_standard.plot.density(ax=ax,\n",
    "                             xlim=(-5,5))\n",
    "plt.savefig('./images/tick_volume_dollar_densities.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#sns.kdeplot(time_standard, label=\"Time\", bw=0.30, color='darkred')\n",
    "sns.kdeplot(tick_standard, \n",
    "            ax=ax,\n",
    "            label=\"Tick\", \n",
    "            bw=0.25,\n",
    "            color='darkblue')\n",
    "sns.kdeplot(volume_standard,\n",
    "            ax=ax,\n",
    "            label=\"Volume\",\n",
    "            bw=0.25,\n",
    "            color='green')\n",
    "sns.kdeplot(dollar_standard,\n",
    "            ax=ax,\n",
    "            label=\"Dollar\",\n",
    "            bw=0.25,\n",
    "            linewidth=2,\n",
    "            color='darkcyan')\n",
    "sns.kdeplot(np.random.normal(size=len(tick_standard)),\n",
    "            ax=ax,\n",
    "            label=\"Normal\",\n",
    "            color='black',\n",
    "            linestyle=\"--\")\n",
    "ax.set_xticks(range(-5, 6))\n",
    "ax.set_xlim(-5, 5)\n",
    "plt.savefig('./images/normal_compare.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.2** On a series of E-mini S&P 500 futures tick data, compute dollar bars\n",
    "and dollar imbalance bars. What bar type exhibits greater serial correlation?\n",
    "Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# implement dollar imbalance bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.3** On dollar bar series of E-mini S&P 500 futures and Eurostoxx 50 futures:\n",
    "- **(a)** Apply Section 2.4.2 to compute the {$\\hat{\\omega}_t$} vector used by the ETF trick. (Hint: You will need FX values for EUR/USD at the roll dates.)\n",
    "- **(b)** Derive the time series of the S&P 500/Eurostoxx 50 spread.\n",
    "- **(c)** Confirm that the series is stationary, with an ADF test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.4** Form E-mini S&P 500 futures dollar bars:\n",
    "- **(a)** Compute Bollinger bands of width 5% around a rolling moving average. Count how many times prices cross the bands out (from within the bands to outside the bands).\n",
    "- **(b)** Now sample those bars using a CUSUM filter, where {$y_t$} are returns and h = 0.05. How many samples do you get?\n",
    "- **(c)** Compute the rolling standard deviation of the two-sampled series. Which one is least heteroscedastic? What is the reason for these results?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(a)** Compute Bollinger bands of width 5% around a rolling moving average. Count how many times prices cross the bands out (from within the bands to outside the bands)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = 100_000\n",
    "dollar_df = ml.dollar_bar(df, m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Traditional Bollinger Bands consist of a N-period moving average (MA), an upper band at K times an N-period standard deviation  above the moving average (MA + Kσ) and a lower band at K times an N-period standard deviation below the moving average (MA − Kσ). , a typical values for N and K are 20 and 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 20\n",
    "K = 2\n",
    "ma = dollar_df.price.rolling(N).mean()\n",
    "sigma = dollar_df.price.rolling(N).std()\n",
    "upper_band = ma + K*sigma\n",
    "lower_band = ma - K*sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "plt.plot(dollar_df.iloc[-100:].price, marker='*')\n",
    "plt.plot(ma.iloc[-100:])\n",
    "plt.plot(upper_band.iloc[-100:], color='k')\n",
    "plt.plot(lower_band.iloc[-100:], color='k')\n",
    "plt.savefig('./images/bollinger_bands.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The exercise says compute Bollinger bands of width 5% around a rolling moving average. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 20\n",
    "ma = dollar_df.price.rolling(N).mean()\n",
    "price_pct = 0.001\n",
    "upper_band = ma + ma*price_pct\n",
    "lower_band = ma - ma*price_pct\n",
    "bb_dataframe = pd.concat([dollar_df.price, ma, upper_band, lower_band], axis=1).dropna()\n",
    "bb_dataframe.columns=['price','ma','upper_band','lower_band']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "plt.plot(bb_dataframe) # color=['b','r', 'k', 'k']\n",
    "plt.savefig('./images/bollinger_2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_date='2018-03-26 10'\n",
    "e_date='2018-03-26 16'\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "plt.plot(bb_dataframe.loc[s_date:e_date]) # color=['b','r', 'k', 'k']\n",
    "plt.savefig('./images/bollinger_3.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count how many times prices cross the bands out (from within the bands to outside the bands)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75328, 6)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dollar_df[dollar_df.price > upper_band].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72816, 6)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dollar_df[dollar_df.price < lower_band].shape  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_up_cross(df):\n",
    "    crit1 = df.price.shift(1) < df.upper_band.shift(1)  \n",
    "    crit2 = df.price > df.upper_band\n",
    "    return df.price[(crit1) & (crit2)]\n",
    "\n",
    "def get_down_cross(df): \n",
    "    crit1 = df.price.shift(1) > df.lower_band.shift(1) \n",
    "    crit2 = df.price < df.lower_band\n",
    "    return df.price[(crit1) & (crit2)]\n",
    "\n",
    "bb_down = get_down_cross(bb_dataframe)\n",
    "bb_up = get_up_cross(bb_dataframe) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12851,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_down.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13210,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_up.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1)\n",
    "plt.plot(bb_dataframe) #color=['b','r', 'k', 'k'])\n",
    "plt.plot(bb_up, marker='^', ms=10, ls='')\n",
    "plt.plot(bb_down, marker='v', ms=10, ls='')\n",
    "plt.savefig('./images/bollinger_4.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_date='2018-03-26 10'\n",
    "e_date='2018-03-26 16'\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "plt.plot(df.loc[s_date:e_date].price, color='b', marker='o', alpha=0.4, ms=2)\n",
    "\n",
    "plt.plot(bb_dataframe.loc[s_date:e_date].price, marker='o', ms=2)\n",
    "plt.plot(bb_dataframe.loc[s_date:e_date].upper_band, color='k')\n",
    "plt.plot(bb_dataframe.loc[s_date:e_date].lower_band, color='k')\n",
    "plt.plot(bb_up.loc[s_date:e_date], color='g', marker='^', ms=10, ls='')\n",
    "plt.plot(bb_down.loc[s_date:e_date], color='r', marker='v', ms=10, ls='')\n",
    "plt.savefig('./images/bollinger_5.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.5** Using the bars from exercise 4:\n",
    "- **(a)** Sample bars using the CUSUM filter, where {$y_t$} are absolute returns and h = 0.05.\n",
    "- **(b)** Compute the rolling standard deviation of the sampled bars.\n",
    "- **(c)** Compare this result with the results from exercise 4. What procedure delivered the least heteroscedastic sample? Why?"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}